Part 6: Convolutional Neural Networks in hls4ml#
In this notebook you will learn how to train a pruned and quantized convolutional neural network (CNN) and deploy it using hls4ml. For this exercise, we will use the Street View House Numbers (SVHN) Dataset (http://ufldl.stanford.edu/housenumbers/).
The SVHN dataset consists of real-world images of house numbers extracted from Google Street View images. The format is similar to that of the MNIST dataset, but is a much more challenging real-world problem, as illustrated by the examples shown below.
All the images are in RGB format and have been cropped to 32x32 pixels. Unlike MNIST, more than one digit can be present in the same image and in these cases, the center digit is used to assign a label to the image. Each image can belong to one of 10 classes, corresponding to digits 0 through 9.
The SVHN dataset consists of 73,257 images for training (and 531,131 extra samples that are easier to classify and can be used as additional training data) and 26,032 images for testing.
Start with the neccessary imports#
import matplotlib.pyplot as plt
import numpy as np
import time
import tensorflow.compat.v2 as tf
import tensorflow_datasets as tfds
2023-06-24 05:58:38.198481: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: SSE4.1 SSE4.2 AVX AVX2 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
/usr/share/miniconda/envs/hls4ml-tutorial/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
from .autonotebook import tqdm as notebook_tqdm
Fetch the SVHN dataset using Tensorflow Dataset#
In this part we will fetch the trainining, validation and test dataset using Tensorflow Datasets (https://www.tensorflow.org/datasets). We will not use the ‘extra’ training in order to save time, but you could fetch it by adding split='train[:90%]+extra'. We will use the first 90% of the training data for training and the last 10% for validation.
ds_train, info = tfds.load('svhn_cropped', split='train[:90%]', with_info=True, as_supervised=True)
ds_test = tfds.load('svhn_cropped', split='test', shuffle_files=True, as_supervised=True)
ds_val = tfds.load('svhn_cropped', split='train[-10%:]', shuffle_files=True, as_supervised=True)
assert isinstance(ds_train, tf.data.Dataset)
train_size = int(info.splits['train'].num_examples)
input_shape = info.features['image'].shape
n_classes = info.features['label'].num_classes
print('Training on {} samples of input shape {}, belonging to {} classes'.format(train_size, input_shape, n_classes))
fig = tfds.show_examples(ds_train, info)
Downloading and preparing dataset Unknown size (download: Unknown size, generated: Unknown size, total: Unknown size) to /home/runner/tensorflow_datasets/svhn_cropped/3.0.0...
2023-06-24 05:58:40.905611: W tensorflow/core/platform/cloud/google_auth_provider.cc:184] All attempts to get a Google authentication bearer token failed, returning an empty token. Retrieving token from files failed with "NOT_FOUND: Could not locate the credentials file.". Retrieving token from GCE failed with "FAILED_PRECONDITION: Error executing an HTTP request: libcurl code 6 meaning 'Couldn't resolve host name', error details: Could not resolve host: metadata".
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Dl Size...: 7%|▋ | 104/1501 [00:02<00:23, 59.90 MiB/s]
Dl Completed...: 0%| | 0/3 [00:02<?, ? url/s]
Dl Size...: 7%|▋ | 105/1501 [00:02<00:23, 59.90 MiB/s]
Dl Completed...: 0%| | 0/3 [00:02<?, ? url/s]
Dl Size...: 7%|▋ | 106/1501 [00:02<00:23, 59.90 MiB/s]
Dl Completed...: 0%| | 0/3 [00:02<?, ? url/s]
Dl Size...: 7%|▋ | 107/1501 [00:02<00:23, 59.90 MiB/s]
Dl Size...: 7%|▋ | 108/1501 [00:02<00:23, 58.15 MiB/s]
Dl Completed...: 0%| | 0/3 [00:02<?, ? url/s]
Dl Size...: 7%|▋ | 108/1501 [00:02<00:23, 58.15 MiB/s]
Dl Completed...: 0%| | 0/3 [00:02<?, ? url/s]
Dl Size...: 7%|▋ | 109/1501 [00:02<00:23, 58.15 MiB/s]
Dl Completed...: 0%| | 0/3 [00:02<?, ? url/s]
Dl Size...: 7%|▋ | 110/1501 [00:02<00:23, 58.15 MiB/s]
Dl Completed...: 0%| | 0/3 [00:02<?, ? url/s]
Dl Size...: 7%|▋ | 111/1501 [00:02<00:23, 58.15 MiB/s]
Dl Completed...: 0%| | 0/3 [00:02<?, ? url/s]
Dl Size...: 7%|▋ | 112/1501 [00:02<00:23, 58.15 MiB/s]
Dl Completed...: 0%| | 0/3 [00:02<?, ? url/s]
Dl Size...: 8%|▊ | 113/1501 [00:02<00:23, 58.15 MiB/s]
Dl Completed...: 0%| | 0/3 [00:02<?, ? url/s]
Dl Size...: 8%|▊ | 114/1501 [00:02<00:23, 58.15 MiB/s]
Dl Size...: 8%|▊ | 115/1501 [00:02<00:24, 56.60 MiB/s]
Dl Completed...: 0%| | 0/3 [00:02<?, ? url/s]
Dl Size...: 8%|▊ | 115/1501 [00:02<00:24, 56.60 MiB/s]
Dl Completed...: 0%| | 0/3 [00:02<?, ? url/s]
Dl Size...: 8%|▊ | 116/1501 [00:02<00:24, 56.60 MiB/s]
Dl Completed...: 0%| | 0/3 [00:02<?, ? url/s]
Dl Size...: 8%|▊ | 117/1501 [00:02<00:24, 56.60 MiB/s]
Dl Completed...: 0%| | 0/3 [00:02<?, ? url/s]
Dl Size...: 8%|▊ | 118/1501 [00:02<00:24, 56.60 MiB/s]
Dl Completed...: 0%| | 0/3 [00:03<?, ? url/s]
Dl Size...: 8%|▊ | 119/1501 [00:03<00:24, 56.60 MiB/s]
Dl Completed...: 0%| | 0/3 [00:03<?, ? url/s]
Dl Size...: 8%|▊ | 120/1501 [00:03<00:24, 56.60 MiB/s]
Dl Completed...: 0%| | 0/3 [00:03<?, ? url/s]
Dl Size...: 8%|▊ | 121/1501 [00:03<00:24, 56.60 MiB/s]
Dl Size...: 8%|▊ | 122/1501 [00:03<00:24, 57.39 MiB/s]
Dl Completed...: 0%| | 0/3 [00:03<?, ? url/s]
Dl Size...: 8%|▊ | 122/1501 [00:03<00:24, 57.39 MiB/s]
Dl Completed...: 0%| | 0/3 [00:03<?, ? url/s]
Dl Size...: 8%|▊ | 123/1501 [00:03<00:24, 57.39 MiB/s]
Dl Completed...: 0%| | 0/3 [00:03<?, ? url/s]
Dl Size...: 8%|▊ | 124/1501 [00:03<00:23, 57.39 MiB/s]
Dl Completed...: 0%| | 0/3 [00:03<?, ? url/s]
Dl Size...: 8%|▊ | 125/1501 [00:03<00:23, 57.39 MiB/s]
Dl Completed...: 0%| | 0/3 [00:03<?, ? url/s]
Dl Size...: 8%|▊ | 126/1501 [00:03<00:23, 57.39 MiB/s]
Dl Completed...: 0%| | 0/3 [00:03<?, ? url/s]
Dl Size...: 8%|▊ | 127/1501 [00:03<00:23, 57.39 MiB/s]
Dl Size...: 9%|▊ | 128/1501 [00:03<00:23, 57.52 MiB/s]
Dl Completed...: 0%| | 0/3 [00:03<?, ? url/s]
Dl Size...: 9%|▊ | 128/1501 [00:03<00:23, 57.52 MiB/s]
Dl Completed...: 0%| | 0/3 [00:03<?, ? url/s]
Dl Size...: 9%|▊ | 129/1501 [00:03<00:23, 57.52 MiB/s]
Dl Completed...: 0%| | 0/3 [00:03<?, ? url/s]
Dl Size...: 9%|▊ | 130/1501 [00:03<00:23, 57.52 MiB/s]
Dl Completed...: 0%| | 0/3 [00:03<?, ? url/s]
Dl Size...: 9%|▊ | 131/1501 [00:03<00:23, 57.52 MiB/s]
Dl Completed...: 0%| | 0/3 [00:03<?, ? url/s]
Dl Size...: 9%|▉ | 132/1501 [00:03<00:23, 57.52 MiB/s]
Dl Completed...: 0%| | 0/3 [00:03<?, ? url/s]
Dl Size...: 9%|▉ | 133/1501 [00:03<00:23, 57.52 MiB/s]
Dl Size...: 9%|▉ | 134/1501 [00:03<00:24, 55.63 MiB/s]
Dl Completed...: 0%| | 0/3 [00:03<?, ? url/s]
Dl Size...: 9%|▉ | 134/1501 [00:03<00:24, 55.63 MiB/s]
Dl Completed...: 0%| | 0/3 [00:03<?, ? url/s]
Dl Size...: 9%|▉ | 135/1501 [00:03<00:24, 55.63 MiB/s]
Dl Completed...: 0%| | 0/3 [00:03<?, ? url/s]
Dl Size...: 9%|▉ | 136/1501 [00:03<00:24, 55.63 MiB/s]
Dl Completed...: 0%| | 0/3 [00:03<?, ? url/s]
Dl Size...: 9%|▉ | 137/1501 [00:03<00:24, 55.63 MiB/s]
Dl Completed...: 0%| | 0/3 [00:03<?, ? url/s]
Dl Size...: 9%|▉ | 138/1501 [00:03<00:24, 55.63 MiB/s]
Dl Completed...: 0%| | 0/3 [00:03<?, ? url/s]
Dl Size...: 9%|▉ | 139/1501 [00:03<00:24, 55.63 MiB/s]
Dl Completed...: 0%| | 0/3 [00:03<?, ? url/s]
Dl Size...: 9%|▉ | 140/1501 [00:03<00:24, 55.63 MiB/s]
Dl Size...: 9%|▉ | 141/1501 [00:03<00:24, 55.30 MiB/s]
Dl Completed...: 0%| | 0/3 [00:03<?, ? url/s]
Dl Size...: 9%|▉ | 141/1501 [00:03<00:24, 55.30 MiB/s]
Dl Completed...: 0%| | 0/3 [00:03<?, ? url/s]
Dl Size...: 9%|▉ | 142/1501 [00:03<00:24, 55.30 MiB/s]
Dl Completed...: 0%| | 0/3 [00:03<?, ? url/s]
Dl Size...: 10%|▉ | 143/1501 [00:03<00:24, 55.30 MiB/s]
Dl Completed...: 0%| | 0/3 [00:03<?, ? url/s]
Dl Size...: 10%|▉ | 144/1501 [00:03<00:24, 55.30 MiB/s]
Dl Completed...: 0%| | 0/3 [00:03<?, ? url/s]
Dl Size...: 10%|▉ | 145/1501 [00:03<00:24, 55.30 MiB/s]
Dl Completed...: 0%| | 0/3 [00:03<?, ? url/s]
Dl Size...: 10%|▉ | 146/1501 [00:03<00:24, 55.30 MiB/s]
Dl Size...: 10%|▉ | 147/1501 [00:03<00:24, 56.38 MiB/s]
Dl Completed...: 0%| | 0/3 [00:03<?, ? url/s]
Dl Size...: 10%|▉ | 147/1501 [00:03<00:24, 56.38 MiB/s]
Dl Completed...: 0%| | 0/3 [00:03<?, ? url/s]
Dl Size...: 10%|▉ | 148/1501 [00:03<00:23, 56.38 MiB/s]
Dl Completed...: 0%| | 0/3 [00:03<?, ? url/s]
Dl Size...: 10%|▉ | 149/1501 [00:03<00:23, 56.38 MiB/s]
Dl Completed...: 0%| | 0/3 [00:03<?, ? url/s]
Dl Size...: 10%|▉ | 150/1501 [00:03<00:23, 56.38 MiB/s]
Dl Completed...: 0%| | 0/3 [00:03<?, ? url/s]
Dl Size...: 10%|█ | 151/1501 [00:03<00:23, 56.38 MiB/s]
Dl Completed...: 0%| | 0/3 [00:03<?, ? url/s]
Dl Size...: 10%|█ | 152/1501 [00:03<00:23, 56.38 MiB/s]
Dl Size...: 10%|█ | 153/1501 [00:03<00:24, 55.73 MiB/s]
Dl Completed...: 0%| | 0/3 [00:03<?, ? url/s]
Dl Size...: 10%|█ | 153/1501 [00:03<00:24, 55.73 MiB/s]
Dl Completed...: 0%| | 0/3 [00:03<?, ? url/s]
Dl Size...: 10%|█ | 154/1501 [00:03<00:24, 55.73 MiB/s]
Dl Completed...: 0%| | 0/3 [00:03<?, ? url/s]
Dl Size...: 10%|█ | 155/1501 [00:03<00:24, 55.73 MiB/s]
Dl Completed...: 0%| | 0/3 [00:03<?, ? url/s]
Dl Size...: 10%|█ | 156/1501 [00:03<00:24, 55.73 MiB/s]
Dl Completed...: 0%| | 0/3 [00:03<?, ? url/s]
Dl Size...: 10%|█ | 157/1501 [00:03<00:24, 55.73 MiB/s]
Dl Completed...: 0%| | 0/3 [00:03<?, ? url/s]
Dl Size...: 11%|█ | 158/1501 [00:03<00:24, 55.73 MiB/s]
Dl Size...: 11%|█ | 159/1501 [00:03<00:24, 55.86 MiB/s]
Dl Completed...: 0%| | 0/3 [00:03<?, ? url/s]
Dl Size...: 11%|█ | 159/1501 [00:03<00:24, 55.86 MiB/s]
Dl Completed...: 0%| | 0/3 [00:03<?, ? url/s]
Dl Size...: 11%|█ | 160/1501 [00:03<00:24, 55.86 MiB/s]
Dl Completed...: 0%| | 0/3 [00:03<?, ? url/s]
Dl Size...: 11%|█ | 161/1501 [00:03<00:23, 55.86 MiB/s]
Dl Completed...: 0%| | 0/3 [00:03<?, ? url/s]
Dl Size...: 11%|█ | 162/1501 [00:03<00:23, 55.86 MiB/s]
Dl Completed...: 0%| | 0/3 [00:03<?, ? url/s]
Dl Size...: 11%|█ | 163/1501 [00:03<00:23, 55.86 MiB/s]
Dl Completed...: 0%| | 0/3 [00:03<?, ? url/s]
Dl Size...: 11%|█ | 164/1501 [00:03<00:23, 55.86 MiB/s]
Dl Size...: 11%|█ | 165/1501 [00:03<00:24, 55.66 MiB/s]
Dl Completed...: 0%| | 0/3 [00:03<?, ? url/s]
Dl Size...: 11%|█ | 165/1501 [00:03<00:24, 55.66 MiB/s]
Dl Completed...: 0%| | 0/3 [00:03<?, ? url/s]
Dl Size...: 11%|█ | 166/1501 [00:03<00:23, 55.66 MiB/s]
Dl Completed...: 0%| | 0/3 [00:03<?, ? url/s]
Dl Size...: 11%|█ | 167/1501 [00:03<00:23, 55.66 MiB/s]
Dl Completed...: 0%| | 0/3 [00:03<?, ? url/s]
Dl Size...: 11%|█ | 168/1501 [00:03<00:23, 55.66 MiB/s]
Dl Completed...: 0%| | 0/3 [00:03<?, ? url/s]
Dl Size...: 11%|█▏ | 169/1501 [00:03<00:23, 55.66 MiB/s]
Dl Completed...: 0%| | 0/3 [00:03<?, ? url/s]
Dl Size...: 11%|█▏ | 170/1501 [00:03<00:23, 55.66 MiB/s]
Dl Size...: 11%|█▏ | 171/1501 [00:03<00:23, 55.98 MiB/s]
Dl Completed...: 0%| | 0/3 [00:03<?, ? url/s]
Dl Size...: 11%|█▏ | 171/1501 [00:03<00:23, 55.98 MiB/s]
Dl Completed...: 0%| | 0/3 [00:03<?, ? url/s]
Dl Size...: 11%|█▏ | 172/1501 [00:03<00:23, 55.98 MiB/s]
Dl Completed...: 0%| | 0/3 [00:03<?, ? url/s]
Dl Size...: 12%|█▏ | 173/1501 [00:03<00:23, 55.98 MiB/s]
Dl Completed...: 0%| | 0/3 [00:03<?, ? url/s]
Dl Size...: 12%|█▏ | 174/1501 [00:03<00:23, 55.98 MiB/s]
Dl Completed...: 0%| | 0/3 [00:04<?, ? url/s]
Dl Size...: 12%|█▏ | 175/1501 [00:04<00:23, 55.98 MiB/s]
Dl Completed...: 0%| | 0/3 [00:04<?, ? url/s]
Dl Size...: 12%|█▏ | 176/1501 [00:04<00:23, 55.98 MiB/s]
Dl Completed...: 0%| | 0/3 [00:04<?, ? url/s]
Dl Size...: 12%|█▏ | 177/1501 [00:04<00:23, 55.98 MiB/s]
Dl Size...: 12%|█▏ | 178/1501 [00:04<00:23, 56.53 MiB/s]
Dl Completed...: 0%| | 0/3 [00:04<?, ? url/s]
Dl Size...: 12%|█▏ | 178/1501 [00:04<00:23, 56.53 MiB/s]
Dl Completed...: 0%| | 0/3 [00:04<?, ? url/s]
Dl Size...: 12%|█▏ | 179/1501 [00:04<00:23, 56.53 MiB/s]
Dl Completed...: 0%| | 0/3 [00:04<?, ? url/s]
Dl Size...: 12%|█▏ | 180/1501 [00:04<00:23, 56.53 MiB/s]
Dl Completed...: 0%| | 0/3 [00:04<?, ? url/s]
Dl Size...: 12%|█▏ | 181/1501 [00:04<00:23, 56.53 MiB/s]
Dl Completed...: 0%| | 0/3 [00:04<?, ? url/s]
Dl Size...: 12%|█▏ | 182/1501 [00:04<00:23, 56.53 MiB/s]
Dl Completed...: 0%| | 0/3 [00:04<?, ? url/s]
Dl Size...: 12%|█▏ | 183/1501 [00:04<00:23, 56.53 MiB/s]
Dl Size...: 12%|█▏ | 184/1501 [00:04<00:23, 57.02 MiB/s]
Dl Completed...: 0%| | 0/3 [00:04<?, ? url/s]
Dl Size...: 12%|█▏ | 184/1501 [00:04<00:23, 57.02 MiB/s]
Dl Completed...: 0%| | 0/3 [00:04<?, ? url/s]
Dl Size...: 12%|█▏ | 185/1501 [00:04<00:23, 57.02 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:04<00:08, 4.20s/ url]
Dl Completed...: 33%|███▎ | 1/3 [00:04<00:08, 4.20s/ url]
Dl Size...: 12%|█▏ | 185/1501 [00:04<00:23, 57.02 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:04<00:08, 4.20s/ url]
Dl Size...: 12%|█▏ | 186/1501 [00:04<00:23, 57.02 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:04<00:08, 4.20s/ url]
Dl Size...: 12%|█▏ | 187/1501 [00:04<00:23, 57.02 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:04<00:08, 4.20s/ url]
Dl Size...: 13%|█▎ | 188/1501 [00:04<00:23, 57.02 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:04<00:08, 4.20s/ url]
Dl Size...: 13%|█▎ | 189/1501 [00:04<00:23, 57.02 MiB/s]
Dl Size...: 13%|█▎ | 190/1501 [00:04<00:23, 56.32 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:04<00:08, 4.20s/ url]
Dl Size...: 13%|█▎ | 190/1501 [00:04<00:23, 56.32 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:04<00:08, 4.20s/ url]
Dl Size...: 13%|█▎ | 191/1501 [00:04<00:23, 56.32 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:04<00:08, 4.20s/ url]
Dl Size...: 13%|█▎ | 192/1501 [00:04<00:23, 56.32 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:04<00:08, 4.20s/ url]
Dl Size...: 13%|█▎ | 193/1501 [00:04<00:23, 56.32 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:04<00:08, 4.20s/ url]
Dl Size...: 13%|█▎ | 194/1501 [00:04<00:23, 56.32 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:04<00:08, 4.20s/ url]
Dl Size...: 13%|█▎ | 195/1501 [00:04<00:23, 56.32 MiB/s]
Dl Size...: 13%|█▎ | 196/1501 [00:04<00:26, 49.87 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:04<00:08, 4.20s/ url]
Dl Size...: 13%|█▎ | 196/1501 [00:04<00:26, 49.87 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:04<00:08, 4.20s/ url]
Dl Size...: 13%|█▎ | 197/1501 [00:04<00:26, 49.87 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:04<00:08, 4.20s/ url]
Dl Size...: 13%|█▎ | 198/1501 [00:04<00:26, 49.87 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:04<00:08, 4.20s/ url]
Dl Size...: 13%|█▎ | 199/1501 [00:04<00:26, 49.87 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:04<00:08, 4.20s/ url]
Dl Size...: 13%|█▎ | 200/1501 [00:04<00:26, 49.87 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:04<00:08, 4.20s/ url]
Dl Size...: 13%|█▎ | 201/1501 [00:04<00:26, 49.87 MiB/s]
Dl Size...: 13%|█▎ | 202/1501 [00:04<00:27, 47.17 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:04<00:08, 4.20s/ url]
Dl Size...: 13%|█▎ | 202/1501 [00:04<00:27, 47.17 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:04<00:08, 4.20s/ url]
Dl Size...: 14%|█▎ | 203/1501 [00:04<00:27, 47.17 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:04<00:08, 4.20s/ url]
Dl Size...: 14%|█▎ | 204/1501 [00:04<00:27, 47.17 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:04<00:08, 4.20s/ url]
Dl Size...: 14%|█▎ | 205/1501 [00:04<00:27, 47.17 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:04<00:08, 4.20s/ url]
Dl Size...: 14%|█▎ | 206/1501 [00:04<00:27, 47.17 MiB/s]
Dl Size...: 14%|█▍ | 207/1501 [00:04<00:29, 44.26 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:04<00:08, 4.20s/ url]
Dl Size...: 14%|█▍ | 207/1501 [00:04<00:29, 44.26 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:04<00:08, 4.20s/ url]
Dl Size...: 14%|█▍ | 208/1501 [00:04<00:29, 44.26 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:04<00:08, 4.20s/ url]
Dl Size...: 14%|█▍ | 209/1501 [00:04<00:29, 44.26 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:04<00:08, 4.20s/ url]
Dl Size...: 14%|█▍ | 210/1501 [00:04<00:29, 44.26 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:04<00:08, 4.20s/ url]
Dl Size...: 14%|█▍ | 211/1501 [00:04<00:29, 44.26 MiB/s]
Dl Size...: 14%|█▍ | 212/1501 [00:04<00:30, 42.51 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:04<00:08, 4.20s/ url]
Dl Size...: 14%|█▍ | 212/1501 [00:04<00:30, 42.51 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:04<00:08, 4.20s/ url]
Dl Size...: 14%|█▍ | 213/1501 [00:04<00:30, 42.51 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:04<00:08, 4.20s/ url]
Dl Size...: 14%|█▍ | 214/1501 [00:04<00:30, 42.51 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:04<00:08, 4.20s/ url]
Dl Size...: 14%|█▍ | 215/1501 [00:04<00:30, 42.51 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:04<00:08, 4.20s/ url]
Dl Size...: 14%|█▍ | 216/1501 [00:04<00:30, 42.51 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:04<00:08, 4.20s/ url]
Dl Size...: 14%|█▍ | 217/1501 [00:04<00:30, 42.51 MiB/s]
Dl Size...: 15%|█▍ | 218/1501 [00:04<00:28, 44.74 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:04<00:08, 4.20s/ url]
Dl Size...: 15%|█▍ | 218/1501 [00:04<00:28, 44.74 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:04<00:08, 4.20s/ url]
Dl Size...: 15%|█▍ | 219/1501 [00:04<00:28, 44.74 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:04<00:08, 4.20s/ url]
Dl Size...: 15%|█▍ | 220/1501 [00:04<00:28, 44.74 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:05<00:08, 4.20s/ url]
Dl Size...: 15%|█▍ | 221/1501 [00:05<00:28, 44.74 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:05<00:08, 4.20s/ url]
Dl Size...: 15%|█▍ | 222/1501 [00:05<00:28, 44.74 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:05<00:08, 4.20s/ url]
Dl Size...: 15%|█▍ | 223/1501 [00:05<00:28, 44.74 MiB/s]
Dl Size...: 15%|█▍ | 224/1501 [00:05<00:26, 47.74 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:05<00:08, 4.20s/ url]
Dl Size...: 15%|█▍ | 224/1501 [00:05<00:26, 47.74 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:05<00:08, 4.20s/ url]
Dl Size...: 15%|█▍ | 225/1501 [00:05<00:26, 47.74 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:05<00:08, 4.20s/ url]
Dl Size...: 15%|█▌ | 226/1501 [00:05<00:26, 47.74 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:05<00:08, 4.20s/ url]
Dl Size...: 15%|█▌ | 227/1501 [00:05<00:26, 47.74 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:05<00:08, 4.20s/ url]
Dl Size...: 15%|█▌ | 228/1501 [00:05<00:26, 47.74 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:05<00:08, 4.20s/ url]
Dl Size...: 15%|█▌ | 229/1501 [00:05<00:26, 47.74 MiB/s]
Dl Size...: 15%|█▌ | 230/1501 [00:05<00:25, 49.09 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:05<00:08, 4.20s/ url]
Dl Size...: 15%|█▌ | 230/1501 [00:05<00:25, 49.09 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:05<00:08, 4.20s/ url]
Dl Size...: 15%|█▌ | 231/1501 [00:05<00:25, 49.09 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:05<00:08, 4.20s/ url]
Dl Size...: 15%|█▌ | 232/1501 [00:05<00:25, 49.09 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:05<00:08, 4.20s/ url]
Dl Size...: 16%|█▌ | 233/1501 [00:05<00:25, 49.09 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:05<00:08, 4.20s/ url]
Dl Size...: 16%|█▌ | 234/1501 [00:05<00:25, 49.09 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:05<00:08, 4.20s/ url]
Dl Size...: 16%|█▌ | 235/1501 [00:05<00:25, 49.09 MiB/s]
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Dl Completed...: 33%|███▎ | 1/3 [00:05<00:08, 4.20s/ url]
Dl Size...: 16%|█▌ | 236/1501 [00:05<00:24, 50.93 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:05<00:08, 4.20s/ url]
Dl Size...: 16%|█▌ | 237/1501 [00:05<00:24, 50.93 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:05<00:08, 4.20s/ url]
Dl Size...: 16%|█▌ | 238/1501 [00:05<00:24, 50.93 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:05<00:08, 4.20s/ url]
Dl Size...: 16%|█▌ | 239/1501 [00:05<00:24, 50.93 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:05<00:08, 4.20s/ url]
Dl Size...: 16%|█▌ | 240/1501 [00:05<00:24, 50.93 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:05<00:08, 4.20s/ url]
Dl Size...: 16%|█▌ | 241/1501 [00:05<00:24, 50.93 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:05<00:08, 4.20s/ url]
Dl Size...: 16%|█▌ | 242/1501 [00:05<00:24, 50.93 MiB/s]
Dl Size...: 16%|█▌ | 243/1501 [00:05<00:22, 55.06 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:05<00:08, 4.20s/ url]
Dl Size...: 16%|█▌ | 243/1501 [00:05<00:22, 55.06 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:05<00:08, 4.20s/ url]
Dl Size...: 16%|█▋ | 244/1501 [00:05<00:22, 55.06 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:05<00:08, 4.20s/ url]
Dl Size...: 16%|█▋ | 245/1501 [00:05<00:22, 55.06 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:05<00:08, 4.20s/ url]
Dl Size...: 16%|█▋ | 246/1501 [00:05<00:22, 55.06 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:05<00:08, 4.20s/ url]
Dl Size...: 16%|█▋ | 247/1501 [00:05<00:22, 55.06 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:05<00:08, 4.20s/ url]
Dl Size...: 17%|█▋ | 248/1501 [00:05<00:22, 55.06 MiB/s]
Dl Size...: 17%|█▋ | 249/1501 [00:05<00:22, 55.22 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:05<00:08, 4.20s/ url]
Dl Size...: 17%|█▋ | 249/1501 [00:05<00:22, 55.22 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:05<00:08, 4.20s/ url]
Dl Size...: 17%|█▋ | 250/1501 [00:05<00:22, 55.22 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:05<00:08, 4.20s/ url]
Dl Size...: 17%|█▋ | 251/1501 [00:05<00:22, 55.22 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:05<00:08, 4.20s/ url]
Dl Size...: 17%|█▋ | 252/1501 [00:05<00:22, 55.22 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:05<00:08, 4.20s/ url]
Dl Size...: 17%|█▋ | 253/1501 [00:05<00:22, 55.22 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:05<00:08, 4.20s/ url]
Dl Size...: 17%|█▋ | 254/1501 [00:05<00:22, 55.22 MiB/s]
Dl Size...: 17%|█▋ | 255/1501 [00:05<00:22, 56.28 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:05<00:08, 4.20s/ url]
Dl Size...: 17%|█▋ | 255/1501 [00:05<00:22, 56.28 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:05<00:08, 4.20s/ url]
Dl Size...: 17%|█▋ | 256/1501 [00:05<00:22, 56.28 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:05<00:08, 4.20s/ url]
Dl Size...: 17%|█▋ | 257/1501 [00:05<00:22, 56.28 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:05<00:08, 4.20s/ url]
Dl Size...: 17%|█▋ | 258/1501 [00:05<00:22, 56.28 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:05<00:08, 4.20s/ url]
Dl Size...: 17%|█▋ | 259/1501 [00:05<00:22, 56.28 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:05<00:08, 4.20s/ url]
Dl Size...: 17%|█▋ | 260/1501 [00:05<00:22, 56.28 MiB/s]
Dl Size...: 17%|█▋ | 261/1501 [00:05<00:21, 57.31 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:05<00:08, 4.20s/ url]
Dl Size...: 17%|█▋ | 261/1501 [00:05<00:21, 57.31 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:05<00:08, 4.20s/ url]
Dl Size...: 17%|█▋ | 262/1501 [00:05<00:21, 57.31 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:05<00:08, 4.20s/ url]
Dl Size...: 18%|█▊ | 263/1501 [00:05<00:21, 57.31 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:05<00:08, 4.20s/ url]
Dl Size...: 18%|█▊ | 264/1501 [00:05<00:21, 57.31 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:05<00:08, 4.20s/ url]
Dl Size...: 18%|█▊ | 265/1501 [00:05<00:21, 57.31 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:05<00:08, 4.20s/ url]
Dl Size...: 18%|█▊ | 266/1501 [00:05<00:21, 57.31 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:05<00:08, 4.20s/ url]
Dl Size...: 18%|█▊ | 267/1501 [00:05<00:21, 57.31 MiB/s]
Dl Size...: 18%|█▊ | 268/1501 [00:05<00:21, 58.70 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:05<00:08, 4.20s/ url]
Dl Size...: 18%|█▊ | 268/1501 [00:05<00:21, 58.70 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:05<00:08, 4.20s/ url]
Dl Size...: 18%|█▊ | 269/1501 [00:05<00:20, 58.70 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:05<00:08, 4.20s/ url]
Dl Size...: 18%|█▊ | 270/1501 [00:05<00:20, 58.70 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:05<00:08, 4.20s/ url]
Dl Size...: 18%|█▊ | 271/1501 [00:05<00:20, 58.70 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:05<00:08, 4.20s/ url]
Dl Size...: 18%|█▊ | 272/1501 [00:05<00:20, 58.70 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:05<00:08, 4.20s/ url]
Dl Size...: 18%|█▊ | 273/1501 [00:05<00:20, 58.70 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:05<00:08, 4.20s/ url]
Dl Size...: 18%|█▊ | 274/1501 [00:05<00:20, 58.70 MiB/s]
Dl Size...: 18%|█▊ | 275/1501 [00:05<00:20, 59.63 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:05<00:08, 4.20s/ url]
Dl Size...: 18%|█▊ | 275/1501 [00:05<00:20, 59.63 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:05<00:08, 4.20s/ url]
Dl Size...: 18%|█▊ | 276/1501 [00:05<00:20, 59.63 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:05<00:08, 4.20s/ url]
Dl Size...: 18%|█▊ | 277/1501 [00:05<00:20, 59.63 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:05<00:08, 4.20s/ url]
Dl Size...: 19%|█▊ | 278/1501 [00:05<00:20, 59.63 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:05<00:08, 4.20s/ url]
Dl Size...: 19%|█▊ | 279/1501 [00:05<00:20, 59.63 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:06<00:08, 4.20s/ url]
Dl Size...: 19%|█▊ | 280/1501 [00:06<00:20, 59.63 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:06<00:08, 4.20s/ url]
Dl Size...: 19%|█▊ | 281/1501 [00:06<00:20, 59.63 MiB/s]
Dl Size...: 19%|█▉ | 282/1501 [00:06<00:20, 58.98 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:06<00:08, 4.20s/ url]
Dl Size...: 19%|█▉ | 282/1501 [00:06<00:20, 58.98 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:06<00:08, 4.20s/ url]
Dl Size...: 19%|█▉ | 283/1501 [00:06<00:20, 58.98 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:06<00:08, 4.20s/ url]
Dl Size...: 19%|█▉ | 284/1501 [00:06<00:20, 58.98 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:06<00:08, 4.20s/ url]
Dl Size...: 19%|█▉ | 285/1501 [00:06<00:20, 58.98 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:06<00:08, 4.20s/ url]
Dl Size...: 19%|█▉ | 286/1501 [00:06<00:20, 58.98 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:06<00:08, 4.20s/ url]
Dl Size...: 19%|█▉ | 287/1501 [00:06<00:20, 58.98 MiB/s]
Dl Size...: 19%|█▉ | 288/1501 [00:06<00:20, 58.63 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:06<00:08, 4.20s/ url]
Dl Size...: 19%|█▉ | 288/1501 [00:06<00:20, 58.63 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:06<00:08, 4.20s/ url]
Dl Size...: 19%|█▉ | 289/1501 [00:06<00:20, 58.63 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:06<00:08, 4.20s/ url]
Dl Size...: 19%|█▉ | 290/1501 [00:06<00:20, 58.63 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:06<00:08, 4.20s/ url]
Dl Size...: 19%|█▉ | 291/1501 [00:06<00:20, 58.63 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:06<00:08, 4.20s/ url]
Dl Size...: 19%|█▉ | 292/1501 [00:06<00:20, 58.63 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:06<00:08, 4.20s/ url]
Dl Size...: 20%|█▉ | 293/1501 [00:06<00:20, 58.63 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:06<00:08, 4.20s/ url]
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Dl Completed...: 33%|███▎ | 1/3 [00:06<00:08, 4.20s/ url]
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Dl Completed...: 33%|███▎ | 1/3 [00:06<00:08, 4.20s/ url]
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Dl Completed...: 33%|███▎ | 1/3 [00:06<00:08, 4.20s/ url]
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Dl Completed...: 33%|███▎ | 1/3 [00:06<00:08, 4.20s/ url]
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Dl Completed...: 33%|███▎ | 1/3 [00:06<00:08, 4.20s/ url]
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Dl Completed...: 33%|███▎ | 1/3 [00:06<00:08, 4.20s/ url]
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Dl Completed...: 33%|███▎ | 1/3 [00:06<00:08, 4.20s/ url]
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Dl Completed...: 33%|███▎ | 1/3 [00:06<00:08, 4.20s/ url]
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Dl Completed...: 33%|███▎ | 1/3 [00:06<00:08, 4.20s/ url]
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Dl Completed...: 33%|███▎ | 1/3 [00:06<00:08, 4.20s/ url]
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Dl Completed...: 33%|███▎ | 1/3 [00:06<00:08, 4.20s/ url]
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Dl Completed...: 33%|███▎ | 1/3 [00:06<00:08, 4.20s/ url]
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Dl Completed...: 33%|███▎ | 1/3 [00:06<00:08, 4.20s/ url]
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Dl Completed...: 33%|███▎ | 1/3 [00:06<00:08, 4.20s/ url]
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Dl Completed...: 33%|███▎ | 1/3 [00:06<00:08, 4.20s/ url]
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Dl Completed...: 33%|███▎ | 1/3 [00:06<00:08, 4.20s/ url]
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Dl Completed...: 33%|███▎ | 1/3 [00:06<00:08, 4.20s/ url]
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Dl Completed...: 33%|███▎ | 1/3 [00:06<00:08, 4.20s/ url]
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Dl Completed...: 33%|███▎ | 1/3 [00:06<00:08, 4.20s/ url]
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Dl Completed...: 33%|███▎ | 1/3 [00:06<00:08, 4.20s/ url]
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Dl Completed...: 33%|███▎ | 1/3 [00:06<00:08, 4.20s/ url]
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Dl Completed...: 33%|███▎ | 1/3 [00:06<00:08, 4.20s/ url]
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Dl Size...: 22%|██▏ | 336/1501 [00:06<00:19, 59.67 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:06<00:08, 4.20s/ url]
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Dl Completed...: 33%|███▎ | 1/3 [00:06<00:08, 4.20s/ url]
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Dl Completed...: 33%|███▎ | 1/3 [00:07<00:08, 4.20s/ url]
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Dl Completed...: 33%|███▎ | 1/3 [00:07<00:08, 4.20s/ url]
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Dl Completed...: 67%|██████▋ | 2/3 [00:07<00:03, 3.40s/ url]
Dl Completed...: 67%|██████▋ | 2/3 [00:07<00:03, 3.40s/ url]
Dl Size...: 23%|██▎ | 340/1501 [00:07<00:19, 59.67 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:07<00:03, 3.40s/ url]
Dl Size...: 23%|██▎ | 341/1501 [00:07<00:19, 59.67 MiB/s]
Dl Size...: 23%|██▎ | 342/1501 [00:07<00:20, 56.71 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:07<00:03, 3.40s/ url]
Dl Size...: 23%|██▎ | 342/1501 [00:07<00:20, 56.71 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:07<00:03, 3.40s/ url]
Dl Size...: 23%|██▎ | 343/1501 [00:07<00:20, 56.71 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:07<00:03, 3.40s/ url]
Dl Size...: 23%|██▎ | 344/1501 [00:07<00:20, 56.71 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:07<00:03, 3.40s/ url]
Dl Size...: 23%|██▎ | 345/1501 [00:07<00:20, 56.71 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:07<00:03, 3.40s/ url]
Dl Size...: 23%|██▎ | 346/1501 [00:07<00:20, 56.71 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:07<00:03, 3.40s/ url]
Dl Size...: 23%|██▎ | 347/1501 [00:07<00:20, 56.71 MiB/s]
Dl Size...: 23%|██▎ | 348/1501 [00:07<00:27, 42.62 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:07<00:03, 3.40s/ url]
Dl Size...: 23%|██▎ | 348/1501 [00:07<00:27, 42.62 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:07<00:03, 3.40s/ url]
Dl Size...: 23%|██▎ | 349/1501 [00:07<00:27, 42.62 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:07<00:03, 3.40s/ url]
Dl Size...: 23%|██▎ | 350/1501 [00:07<00:27, 42.62 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:07<00:03, 3.40s/ url]
Dl Size...: 23%|██▎ | 351/1501 [00:07<00:26, 42.62 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:07<00:03, 3.40s/ url]
Dl Size...: 23%|██▎ | 352/1501 [00:07<00:26, 42.62 MiB/s]
Dl Size...: 24%|██▎ | 353/1501 [00:07<00:29, 38.54 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:07<00:03, 3.40s/ url]
Dl Size...: 24%|██▎ | 353/1501 [00:07<00:29, 38.54 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:07<00:03, 3.40s/ url]
Dl Size...: 24%|██▎ | 354/1501 [00:07<00:29, 38.54 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:07<00:03, 3.40s/ url]
Dl Size...: 24%|██▎ | 355/1501 [00:07<00:29, 38.54 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:07<00:03, 3.40s/ url]
Dl Size...: 24%|██▎ | 356/1501 [00:07<00:29, 38.54 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:07<00:03, 3.40s/ url]
Dl Size...: 24%|██▍ | 357/1501 [00:07<00:29, 38.54 MiB/s]
Dl Size...: 24%|██▍ | 358/1501 [00:07<00:32, 35.56 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:07<00:03, 3.40s/ url]
Dl Size...: 24%|██▍ | 358/1501 [00:07<00:32, 35.56 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:07<00:03, 3.40s/ url]
Dl Size...: 24%|██▍ | 359/1501 [00:07<00:32, 35.56 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:07<00:03, 3.40s/ url]
Dl Size...: 24%|██▍ | 360/1501 [00:07<00:32, 35.56 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:07<00:03, 3.40s/ url]
Dl Size...: 24%|██▍ | 361/1501 [00:07<00:32, 35.56 MiB/s]
Dl Size...: 24%|██▍ | 362/1501 [00:07<00:34, 33.43 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:07<00:03, 3.40s/ url]
Dl Size...: 24%|██▍ | 362/1501 [00:07<00:34, 33.43 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:07<00:03, 3.40s/ url]
Dl Size...: 24%|██▍ | 363/1501 [00:07<00:34, 33.43 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:07<00:03, 3.40s/ url]
Dl Size...: 24%|██▍ | 364/1501 [00:07<00:34, 33.43 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:07<00:03, 3.40s/ url]
Dl Size...: 24%|██▍ | 365/1501 [00:07<00:33, 33.43 MiB/s]
Dl Size...: 24%|██▍ | 366/1501 [00:07<00:34, 32.54 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:07<00:03, 3.40s/ url]
Dl Size...: 24%|██▍ | 366/1501 [00:07<00:34, 32.54 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:07<00:03, 3.40s/ url]
Dl Size...: 24%|██▍ | 367/1501 [00:07<00:34, 32.54 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:07<00:03, 3.40s/ url]
Dl Size...: 25%|██▍ | 368/1501 [00:07<00:34, 32.54 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:08<00:03, 3.40s/ url]
Dl Size...: 25%|██▍ | 369/1501 [00:08<00:34, 32.54 MiB/s]
Dl Size...: 25%|██▍ | 370/1501 [00:08<00:36, 31.34 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:08<00:03, 3.40s/ url]
Dl Size...: 25%|██▍ | 370/1501 [00:08<00:36, 31.34 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:08<00:03, 3.40s/ url]
Dl Size...: 25%|██▍ | 371/1501 [00:08<00:36, 31.34 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:08<00:03, 3.40s/ url]
Dl Size...: 25%|██▍ | 372/1501 [00:08<00:36, 31.34 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:08<00:03, 3.40s/ url]
Dl Size...: 25%|██▍ | 373/1501 [00:08<00:35, 31.34 MiB/s]
Dl Size...: 25%|██▍ | 374/1501 [00:08<00:36, 30.60 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:08<00:03, 3.40s/ url]
Dl Size...: 25%|██▍ | 374/1501 [00:08<00:36, 30.60 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:08<00:03, 3.40s/ url]
Dl Size...: 25%|██▍ | 375/1501 [00:08<00:36, 30.60 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:08<00:03, 3.40s/ url]
Dl Size...: 25%|██▌ | 376/1501 [00:08<00:36, 30.60 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:08<00:03, 3.40s/ url]
Dl Size...: 25%|██▌ | 377/1501 [00:08<00:36, 30.60 MiB/s]
Dl Size...: 25%|██▌ | 378/1501 [00:08<00:37, 30.28 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:08<00:03, 3.40s/ url]
Dl Size...: 25%|██▌ | 378/1501 [00:08<00:37, 30.28 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:08<00:03, 3.40s/ url]
Dl Size...: 25%|██▌ | 379/1501 [00:08<00:37, 30.28 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:08<00:03, 3.40s/ url]
Dl Size...: 25%|██▌ | 380/1501 [00:08<00:37, 30.28 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:08<00:03, 3.40s/ url]
Dl Size...: 25%|██▌ | 381/1501 [00:08<00:36, 30.28 MiB/s]
Dl Size...: 25%|██▌ | 382/1501 [00:08<00:37, 30.00 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:08<00:03, 3.40s/ url]
Dl Size...: 25%|██▌ | 382/1501 [00:08<00:37, 30.00 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:08<00:03, 3.40s/ url]
Dl Size...: 26%|██▌ | 383/1501 [00:08<00:37, 30.00 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:08<00:03, 3.40s/ url]
Dl Size...: 26%|██▌ | 384/1501 [00:08<00:37, 30.00 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:08<00:03, 3.40s/ url]
Dl Size...: 26%|██▌ | 385/1501 [00:08<00:37, 30.00 MiB/s]
Dl Size...: 26%|██▌ | 386/1501 [00:08<00:37, 29.90 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:08<00:03, 3.40s/ url]
Dl Size...: 26%|██▌ | 386/1501 [00:08<00:37, 29.90 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:08<00:03, 3.40s/ url]
Dl Size...: 26%|██▌ | 387/1501 [00:08<00:37, 29.90 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:08<00:03, 3.40s/ url]
Dl Size...: 26%|██▌ | 388/1501 [00:08<00:37, 29.90 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:08<00:03, 3.40s/ url]
Dl Size...: 26%|██▌ | 389/1501 [00:08<00:37, 29.90 MiB/s]
Dl Size...: 26%|██▌ | 390/1501 [00:08<00:37, 29.50 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:08<00:03, 3.40s/ url]
Dl Size...: 26%|██▌ | 390/1501 [00:08<00:37, 29.50 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:08<00:03, 3.40s/ url]
Dl Size...: 26%|██▌ | 391/1501 [00:08<00:37, 29.50 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:08<00:03, 3.40s/ url]
Dl Size...: 26%|██▌ | 392/1501 [00:08<00:37, 29.50 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:08<00:03, 3.40s/ url]
Dl Size...: 26%|██▌ | 393/1501 [00:08<00:37, 29.50 MiB/s]
Dl Size...: 26%|██▌ | 394/1501 [00:08<00:36, 30.06 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:08<00:03, 3.40s/ url]
Dl Size...: 26%|██▌ | 394/1501 [00:08<00:36, 30.06 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:08<00:03, 3.40s/ url]
Dl Size...: 26%|██▋ | 395/1501 [00:08<00:36, 30.06 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:08<00:03, 3.40s/ url]
Dl Size...: 26%|██▋ | 396/1501 [00:08<00:36, 30.06 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:08<00:03, 3.40s/ url]
Dl Size...: 26%|██▋ | 397/1501 [00:08<00:36, 30.06 MiB/s]
Dl Size...: 27%|██▋ | 398/1501 [00:09<00:36, 29.85 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:09<00:03, 3.40s/ url]
Dl Size...: 27%|██▋ | 398/1501 [00:09<00:36, 29.85 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:09<00:03, 3.40s/ url]
Dl Size...: 27%|██▋ | 399/1501 [00:09<00:36, 29.85 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:09<00:03, 3.40s/ url]
Dl Size...: 27%|██▋ | 400/1501 [00:09<00:36, 29.85 MiB/s]
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Dl Completed...: 67%|██████▋ | 2/3 [00:09<00:03, 3.40s/ url]
Dl Size...: 27%|██▋ | 401/1501 [00:09<00:37, 29.57 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:09<00:03, 3.40s/ url]
Dl Size...: 27%|██▋ | 402/1501 [00:09<00:37, 29.57 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:09<00:03, 3.40s/ url]
Dl Size...: 27%|██▋ | 403/1501 [00:09<00:37, 29.57 MiB/s]
Dl Size...: 27%|██▋ | 404/1501 [00:09<00:37, 28.95 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:09<00:03, 3.40s/ url]
Dl Size...: 27%|██▋ | 404/1501 [00:09<00:37, 28.95 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:09<00:03, 3.40s/ url]
Dl Size...: 27%|██▋ | 405/1501 [00:09<00:37, 28.95 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:09<00:03, 3.40s/ url]
Dl Size...: 27%|██▋ | 406/1501 [00:09<00:37, 28.95 MiB/s]
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Dl Completed...: 67%|██████▋ | 2/3 [00:09<00:03, 3.40s/ url]
Dl Size...: 27%|██▋ | 407/1501 [00:09<00:38, 28.56 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:09<00:03, 3.40s/ url]
Dl Size...: 27%|██▋ | 408/1501 [00:09<00:38, 28.56 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:09<00:03, 3.40s/ url]
Dl Size...: 27%|██▋ | 409/1501 [00:09<00:38, 28.56 MiB/s]
Dl Size...: 27%|██▋ | 410/1501 [00:09<00:39, 27.95 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:09<00:03, 3.40s/ url]
Dl Size...: 27%|██▋ | 410/1501 [00:09<00:39, 27.95 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:09<00:03, 3.40s/ url]
Dl Size...: 27%|██▋ | 411/1501 [00:09<00:38, 27.95 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:09<00:03, 3.40s/ url]
Dl Size...: 27%|██▋ | 412/1501 [00:09<00:38, 27.95 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:09<00:03, 3.40s/ url]
Dl Size...: 28%|██▊ | 413/1501 [00:09<00:38, 27.95 MiB/s]
Dl Size...: 28%|██▊ | 414/1501 [00:09<00:37, 28.75 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:09<00:03, 3.40s/ url]
Dl Size...: 28%|██▊ | 414/1501 [00:09<00:37, 28.75 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:09<00:03, 3.40s/ url]
Dl Size...: 28%|██▊ | 415/1501 [00:09<00:37, 28.75 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:09<00:03, 3.40s/ url]
Dl Size...: 28%|██▊ | 416/1501 [00:09<00:37, 28.75 MiB/s]
Dl Size...: 28%|██▊ | 417/1501 [00:09<00:37, 28.53 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:09<00:03, 3.40s/ url]
Dl Size...: 28%|██▊ | 417/1501 [00:09<00:37, 28.53 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:09<00:03, 3.40s/ url]
Dl Size...: 28%|██▊ | 418/1501 [00:09<00:37, 28.53 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:09<00:03, 3.40s/ url]
Dl Size...: 28%|██▊ | 419/1501 [00:09<00:37, 28.53 MiB/s]
Dl Size...: 28%|██▊ | 420/1501 [00:09<00:37, 28.80 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:09<00:03, 3.40s/ url]
Dl Size...: 28%|██▊ | 420/1501 [00:09<00:37, 28.80 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:09<00:03, 3.40s/ url]
Dl Size...: 28%|██▊ | 421/1501 [00:09<00:37, 28.80 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:09<00:03, 3.40s/ url]
Dl Size...: 28%|██▊ | 422/1501 [00:09<00:37, 28.80 MiB/s]
Dl Size...: 28%|██▊ | 423/1501 [00:09<00:37, 28.60 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:09<00:03, 3.40s/ url]
Dl Size...: 28%|██▊ | 423/1501 [00:09<00:37, 28.60 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:09<00:03, 3.40s/ url]
Dl Size...: 28%|██▊ | 424/1501 [00:09<00:37, 28.60 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:09<00:03, 3.40s/ url]
Dl Size...: 28%|██▊ | 425/1501 [00:09<00:37, 28.60 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:09<00:03, 3.40s/ url]
Dl Size...: 28%|██▊ | 426/1501 [00:09<00:37, 28.60 MiB/s]
Dl Size...: 28%|██▊ | 427/1501 [00:10<00:37, 28.89 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:10<00:03, 3.40s/ url]
Dl Size...: 28%|██▊ | 427/1501 [00:10<00:37, 28.89 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:10<00:03, 3.40s/ url]
Dl Size...: 29%|██▊ | 428/1501 [00:10<00:37, 28.89 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:10<00:03, 3.40s/ url]
Dl Size...: 29%|██▊ | 429/1501 [00:10<00:37, 28.89 MiB/s]
Dl Size...: 29%|██▊ | 430/1501 [00:10<00:37, 28.52 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:10<00:03, 3.40s/ url]
Dl Size...: 29%|██▊ | 430/1501 [00:10<00:37, 28.52 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:10<00:03, 3.40s/ url]
Dl Size...: 29%|██▊ | 431/1501 [00:10<00:37, 28.52 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:10<00:03, 3.40s/ url]
Dl Size...: 29%|██▉ | 432/1501 [00:10<00:37, 28.52 MiB/s]
Dl Size...: 29%|██▉ | 433/1501 [00:10<00:37, 28.64 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:10<00:03, 3.40s/ url]
Dl Size...: 29%|██▉ | 433/1501 [00:10<00:37, 28.64 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:10<00:03, 3.40s/ url]
Dl Size...: 29%|██▉ | 434/1501 [00:10<00:37, 28.64 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:10<00:03, 3.40s/ url]
Dl Size...: 29%|██▉ | 435/1501 [00:10<00:37, 28.64 MiB/s]
Dl Size...: 29%|██▉ | 436/1501 [00:10<00:36, 28.80 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:10<00:03, 3.40s/ url]
Dl Size...: 29%|██▉ | 436/1501 [00:10<00:36, 28.80 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:10<00:03, 3.40s/ url]
Dl Size...: 29%|██▉ | 437/1501 [00:10<00:36, 28.80 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:10<00:03, 3.40s/ url]
Dl Size...: 29%|██▉ | 438/1501 [00:10<00:36, 28.80 MiB/s]
Dl Size...: 29%|██▉ | 439/1501 [00:10<00:36, 28.84 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:10<00:03, 3.40s/ url]
Dl Size...: 29%|██▉ | 439/1501 [00:10<00:36, 28.84 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:10<00:03, 3.40s/ url]
Dl Size...: 29%|██▉ | 440/1501 [00:10<00:36, 28.84 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:10<00:03, 3.40s/ url]
Dl Size...: 29%|██▉ | 441/1501 [00:10<00:36, 28.84 MiB/s]
Dl Size...: 29%|██▉ | 442/1501 [00:10<00:36, 29.07 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:10<00:03, 3.40s/ url]
Dl Size...: 29%|██▉ | 442/1501 [00:10<00:36, 29.07 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:10<00:03, 3.40s/ url]
Dl Size...: 30%|██▉ | 443/1501 [00:10<00:36, 29.07 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:10<00:03, 3.40s/ url]
Dl Size...: 30%|██▉ | 444/1501 [00:10<00:36, 29.07 MiB/s]
Dl Size...: 30%|██▉ | 445/1501 [00:10<00:36, 29.11 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:10<00:03, 3.40s/ url]
Dl Size...: 30%|██▉ | 445/1501 [00:10<00:36, 29.11 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:10<00:03, 3.40s/ url]
Dl Size...: 30%|██▉ | 446/1501 [00:10<00:36, 29.11 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:10<00:03, 3.40s/ url]
Dl Size...: 30%|██▉ | 447/1501 [00:10<00:36, 29.11 MiB/s]
Dl Size...: 30%|██▉ | 448/1501 [00:10<00:36, 29.11 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:10<00:03, 3.40s/ url]
Dl Size...: 30%|██▉ | 448/1501 [00:10<00:36, 29.11 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:10<00:03, 3.40s/ url]
Dl Size...: 30%|██▉ | 449/1501 [00:10<00:36, 29.11 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:10<00:03, 3.40s/ url]
Dl Size...: 30%|██▉ | 450/1501 [00:10<00:36, 29.11 MiB/s]
Dl Size...: 30%|███ | 451/1501 [00:10<00:38, 27.29 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:10<00:03, 3.40s/ url]
Dl Size...: 30%|███ | 451/1501 [00:10<00:38, 27.29 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:10<00:03, 3.40s/ url]
Dl Size...: 30%|███ | 452/1501 [00:10<00:38, 27.29 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:10<00:03, 3.40s/ url]
Dl Size...: 30%|███ | 453/1501 [00:10<00:38, 27.29 MiB/s]
Dl Size...: 30%|███ | 454/1501 [00:10<00:37, 27.88 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:10<00:03, 3.40s/ url]
Dl Size...: 30%|███ | 454/1501 [00:10<00:37, 27.88 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:11<00:03, 3.40s/ url]
Dl Size...: 30%|███ | 455/1501 [00:11<00:37, 27.88 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:11<00:03, 3.40s/ url]
Dl Size...: 30%|███ | 456/1501 [00:11<00:37, 27.88 MiB/s]
Dl Size...: 30%|███ | 457/1501 [00:11<00:36, 28.29 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:11<00:03, 3.40s/ url]
Dl Size...: 30%|███ | 457/1501 [00:11<00:36, 28.29 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:11<00:03, 3.40s/ url]
Dl Size...: 31%|███ | 458/1501 [00:11<00:36, 28.29 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:11<00:03, 3.40s/ url]
Dl Size...: 31%|███ | 459/1501 [00:11<00:36, 28.29 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:11<00:03, 3.40s/ url]
Dl Size...: 31%|███ | 460/1501 [00:11<00:36, 28.29 MiB/s]
Dl Size...: 31%|███ | 461/1501 [00:11<00:35, 28.95 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:11<00:03, 3.40s/ url]
Dl Size...: 31%|███ | 461/1501 [00:11<00:35, 28.95 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:11<00:03, 3.40s/ url]
Dl Size...: 31%|███ | 462/1501 [00:11<00:35, 28.95 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:11<00:03, 3.40s/ url]
Dl Size...: 31%|███ | 463/1501 [00:11<00:35, 28.95 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:11<00:03, 3.40s/ url]
Dl Size...: 31%|███ | 464/1501 [00:11<00:35, 28.95 MiB/s]
Dl Size...: 31%|███ | 465/1501 [00:11<00:35, 29.25 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:11<00:03, 3.40s/ url]
Dl Size...: 31%|███ | 465/1501 [00:11<00:35, 29.25 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:11<00:03, 3.40s/ url]
Dl Size...: 31%|███ | 466/1501 [00:11<00:35, 29.25 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:11<00:03, 3.40s/ url]
Dl Size...: 31%|███ | 467/1501 [00:11<00:35, 29.25 MiB/s]
Dl Size...: 31%|███ | 468/1501 [00:11<00:35, 29.38 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:11<00:03, 3.40s/ url]
Dl Size...: 31%|███ | 468/1501 [00:11<00:35, 29.38 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:11<00:03, 3.40s/ url]
Dl Size...: 31%|███ | 469/1501 [00:11<00:35, 29.38 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:11<00:03, 3.40s/ url]
Dl Size...: 31%|███▏ | 470/1501 [00:11<00:35, 29.38 MiB/s]
Dl Size...: 31%|███▏ | 471/1501 [00:11<00:35, 29.42 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:11<00:03, 3.40s/ url]
Dl Size...: 31%|███▏ | 471/1501 [00:11<00:35, 29.42 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:11<00:03, 3.40s/ url]
Dl Size...: 31%|███▏ | 472/1501 [00:11<00:34, 29.42 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:11<00:03, 3.40s/ url]
Dl Size...: 32%|███▏ | 473/1501 [00:11<00:34, 29.42 MiB/s]
Dl Size...: 32%|███▏ | 474/1501 [00:11<00:34, 29.38 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:11<00:03, 3.40s/ url]
Dl Size...: 32%|███▏ | 474/1501 [00:11<00:34, 29.38 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:11<00:03, 3.40s/ url]
Dl Size...: 32%|███▏ | 475/1501 [00:11<00:34, 29.38 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:11<00:03, 3.40s/ url]
Dl Size...: 32%|███▏ | 476/1501 [00:11<00:34, 29.38 MiB/s]
Dl Size...: 32%|███▏ | 477/1501 [00:11<00:34, 29.35 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:11<00:03, 3.40s/ url]
Dl Size...: 32%|███▏ | 477/1501 [00:11<00:34, 29.35 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:11<00:03, 3.40s/ url]
Dl Size...: 32%|███▏ | 478/1501 [00:11<00:34, 29.35 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:11<00:03, 3.40s/ url]
Dl Size...: 32%|███▏ | 479/1501 [00:11<00:34, 29.35 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:11<00:03, 3.40s/ url]
Dl Size...: 32%|███▏ | 480/1501 [00:11<00:34, 29.35 MiB/s]
Dl Size...: 32%|███▏ | 481/1501 [00:11<00:34, 29.90 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:11<00:03, 3.40s/ url]
Dl Size...: 32%|███▏ | 481/1501 [00:11<00:34, 29.90 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:11<00:03, 3.40s/ url]
Dl Size...: 32%|███▏ | 482/1501 [00:11<00:34, 29.90 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:11<00:03, 3.40s/ url]
Dl Size...: 32%|███▏ | 483/1501 [00:11<00:34, 29.90 MiB/s]
Dl Size...: 32%|███▏ | 484/1501 [00:11<00:34, 29.64 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:11<00:03, 3.40s/ url]
Dl Size...: 32%|███▏ | 484/1501 [00:11<00:34, 29.64 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:12<00:03, 3.40s/ url]
Dl Size...: 32%|███▏ | 485/1501 [00:12<00:34, 29.64 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:12<00:03, 3.40s/ url]
Dl Size...: 32%|███▏ | 486/1501 [00:12<00:34, 29.64 MiB/s]
Dl Size...: 32%|███▏ | 487/1501 [00:12<00:34, 29.64 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:12<00:03, 3.40s/ url]
Dl Size...: 32%|███▏ | 487/1501 [00:12<00:34, 29.64 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:12<00:03, 3.40s/ url]
Dl Size...: 33%|███▎ | 488/1501 [00:12<00:34, 29.64 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:12<00:03, 3.40s/ url]
Dl Size...: 33%|███▎ | 489/1501 [00:12<00:34, 29.64 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:12<00:03, 3.40s/ url]
Dl Size...: 33%|███▎ | 490/1501 [00:12<00:34, 29.64 MiB/s]
Dl Size...: 33%|███▎ | 491/1501 [00:12<00:34, 29.51 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:12<00:03, 3.40s/ url]
Dl Size...: 33%|███▎ | 491/1501 [00:12<00:34, 29.51 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:12<00:03, 3.40s/ url]
Dl Size...: 33%|███▎ | 492/1501 [00:12<00:34, 29.51 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:12<00:03, 3.40s/ url]
Dl Size...: 33%|███▎ | 493/1501 [00:12<00:34, 29.51 MiB/s]
Dl Size...: 33%|███▎ | 494/1501 [00:12<00:34, 29.60 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:12<00:03, 3.40s/ url]
Dl Size...: 33%|███▎ | 494/1501 [00:12<00:34, 29.60 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:12<00:03, 3.40s/ url]
Dl Size...: 33%|███▎ | 495/1501 [00:12<00:33, 29.60 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:12<00:03, 3.40s/ url]
Dl Size...: 33%|███▎ | 496/1501 [00:12<00:33, 29.60 MiB/s]
Dl Size...: 33%|███▎ | 497/1501 [00:12<00:35, 28.19 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:12<00:03, 3.40s/ url]
Dl Size...: 33%|███▎ | 497/1501 [00:12<00:35, 28.19 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:12<00:03, 3.40s/ url]
Dl Size...: 33%|███▎ | 498/1501 [00:12<00:35, 28.19 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:12<00:03, 3.40s/ url]
Dl Size...: 33%|███▎ | 499/1501 [00:12<00:35, 28.19 MiB/s]
Dl Size...: 33%|███▎ | 500/1501 [00:12<00:34, 28.60 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:12<00:03, 3.40s/ url]
Dl Size...: 33%|███▎ | 500/1501 [00:12<00:34, 28.60 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:12<00:03, 3.40s/ url]
Dl Size...: 33%|███▎ | 501/1501 [00:12<00:34, 28.60 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:12<00:03, 3.40s/ url]
Dl Size...: 33%|███▎ | 502/1501 [00:12<00:34, 28.60 MiB/s]
Dl Size...: 34%|███▎ | 503/1501 [00:12<00:34, 28.89 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:12<00:03, 3.40s/ url]
Dl Size...: 34%|███▎ | 503/1501 [00:12<00:34, 28.89 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:12<00:03, 3.40s/ url]
Dl Size...: 34%|███▎ | 504/1501 [00:12<00:34, 28.89 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:12<00:03, 3.40s/ url]
Dl Size...: 34%|███▎ | 505/1501 [00:12<00:34, 28.89 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:12<00:03, 3.40s/ url]
Dl Size...: 34%|███▎ | 506/1501 [00:12<00:34, 28.89 MiB/s]
Dl Size...: 34%|███▍ | 507/1501 [00:12<00:33, 29.27 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:12<00:03, 3.40s/ url]
Dl Size...: 34%|███▍ | 507/1501 [00:12<00:33, 29.27 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:12<00:03, 3.40s/ url]
Dl Size...: 34%|███▍ | 508/1501 [00:12<00:33, 29.27 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:12<00:03, 3.40s/ url]
Dl Size...: 34%|███▍ | 509/1501 [00:12<00:33, 29.27 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:12<00:03, 3.40s/ url]
Dl Size...: 34%|███▍ | 510/1501 [00:12<00:33, 29.27 MiB/s]
Dl Size...: 34%|███▍ | 511/1501 [00:12<00:33, 29.32 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:12<00:03, 3.40s/ url]
Dl Size...: 34%|███▍ | 511/1501 [00:12<00:33, 29.32 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:12<00:03, 3.40s/ url]
Dl Size...: 34%|███▍ | 512/1501 [00:12<00:33, 29.32 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:12<00:03, 3.40s/ url]
Dl Size...: 34%|███▍ | 513/1501 [00:12<00:33, 29.32 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:13<00:03, 3.40s/ url]
Dl Size...: 34%|███▍ | 514/1501 [00:13<00:33, 29.32 MiB/s]
Dl Size...: 34%|███▍ | 515/1501 [00:13<00:33, 29.70 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:13<00:03, 3.40s/ url]
Dl Size...: 34%|███▍ | 515/1501 [00:13<00:33, 29.70 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:13<00:03, 3.40s/ url]
Dl Size...: 34%|███▍ | 516/1501 [00:13<00:33, 29.70 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:13<00:03, 3.40s/ url]
Dl Size...: 34%|███▍ | 517/1501 [00:13<00:33, 29.70 MiB/s]
Dl Size...: 35%|███▍ | 518/1501 [00:13<00:33, 29.54 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:13<00:03, 3.40s/ url]
Dl Size...: 35%|███▍ | 518/1501 [00:13<00:33, 29.54 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:13<00:03, 3.40s/ url]
Dl Size...: 35%|███▍ | 519/1501 [00:13<00:33, 29.54 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:13<00:03, 3.40s/ url]
Dl Size...: 35%|███▍ | 520/1501 [00:13<00:33, 29.54 MiB/s]
Dl Size...: 35%|███▍ | 521/1501 [00:13<00:33, 29.24 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:13<00:03, 3.40s/ url]
Dl Size...: 35%|███▍ | 521/1501 [00:13<00:33, 29.24 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:13<00:03, 3.40s/ url]
Dl Size...: 35%|███▍ | 522/1501 [00:13<00:33, 29.24 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:13<00:03, 3.40s/ url]
Dl Size...: 35%|███▍ | 523/1501 [00:13<00:33, 29.24 MiB/s]
Dl Size...: 35%|███▍ | 524/1501 [00:13<00:33, 28.90 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:13<00:03, 3.40s/ url]
Dl Size...: 35%|███▍ | 524/1501 [00:13<00:33, 28.90 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:13<00:03, 3.40s/ url]
Dl Size...: 35%|███▍ | 525/1501 [00:13<00:33, 28.90 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:13<00:03, 3.40s/ url]
Dl Size...: 35%|███▌ | 526/1501 [00:13<00:33, 28.90 MiB/s]
Dl Size...: 35%|███▌ | 527/1501 [00:13<00:33, 29.15 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:13<00:03, 3.40s/ url]
Dl Size...: 35%|███▌ | 527/1501 [00:13<00:33, 29.15 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:13<00:03, 3.40s/ url]
Dl Size...: 35%|███▌ | 528/1501 [00:13<00:33, 29.15 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:13<00:03, 3.40s/ url]
Dl Size...: 35%|███▌ | 529/1501 [00:13<00:33, 29.15 MiB/s]
Dl Size...: 35%|███▌ | 530/1501 [00:13<00:33, 28.87 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:13<00:03, 3.40s/ url]
Dl Size...: 35%|███▌ | 530/1501 [00:13<00:33, 28.87 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:13<00:03, 3.40s/ url]
Dl Size...: 35%|███▌ | 531/1501 [00:13<00:33, 28.87 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:13<00:03, 3.40s/ url]
Dl Size...: 35%|███▌ | 532/1501 [00:13<00:33, 28.87 MiB/s]
Dl Size...: 36%|███▌ | 533/1501 [00:13<00:33, 28.84 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:13<00:03, 3.40s/ url]
Dl Size...: 36%|███▌ | 533/1501 [00:13<00:33, 28.84 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:13<00:03, 3.40s/ url]
Dl Size...: 36%|███▌ | 534/1501 [00:13<00:33, 28.84 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:13<00:03, 3.40s/ url]
Dl Size...: 36%|███▌ | 535/1501 [00:13<00:33, 28.84 MiB/s]
Dl Size...: 36%|███▌ | 536/1501 [00:13<00:33, 29.04 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:13<00:03, 3.40s/ url]
Dl Size...: 36%|███▌ | 536/1501 [00:13<00:33, 29.04 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:13<00:03, 3.40s/ url]
Dl Size...: 36%|███▌ | 537/1501 [00:13<00:33, 29.04 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:13<00:03, 3.40s/ url]
Dl Size...: 36%|███▌ | 538/1501 [00:13<00:33, 29.04 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:13<00:03, 3.40s/ url]
Dl Size...: 36%|███▌ | 539/1501 [00:13<00:33, 29.04 MiB/s]
Dl Size...: 36%|███▌ | 540/1501 [00:13<00:32, 29.52 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:13<00:03, 3.40s/ url]
Dl Size...: 36%|███▌ | 540/1501 [00:13<00:32, 29.52 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:13<00:03, 3.40s/ url]
Dl Size...: 36%|███▌ | 541/1501 [00:13<00:32, 29.52 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:13<00:03, 3.40s/ url]
Dl Size...: 36%|███▌ | 542/1501 [00:13<00:32, 29.52 MiB/s]
Dl Size...: 36%|███▌ | 543/1501 [00:14<00:32, 29.55 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:14<00:03, 3.40s/ url]
Dl Size...: 36%|███▌ | 543/1501 [00:14<00:32, 29.55 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:14<00:03, 3.40s/ url]
Dl Size...: 36%|███▌ | 544/1501 [00:14<00:32, 29.55 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:14<00:03, 3.40s/ url]
Dl Size...: 36%|███▋ | 545/1501 [00:14<00:32, 29.55 MiB/s]
Dl Size...: 36%|███▋ | 546/1501 [00:14<00:32, 29.56 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:14<00:03, 3.40s/ url]
Dl Size...: 36%|███▋ | 546/1501 [00:14<00:32, 29.56 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:14<00:03, 3.40s/ url]
Dl Size...: 36%|███▋ | 547/1501 [00:14<00:32, 29.56 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:14<00:03, 3.40s/ url]
Dl Size...: 37%|███▋ | 548/1501 [00:14<00:32, 29.56 MiB/s]
Dl Size...: 37%|███▋ | 549/1501 [00:14<00:32, 29.21 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:14<00:03, 3.40s/ url]
Dl Size...: 37%|███▋ | 549/1501 [00:14<00:32, 29.21 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:14<00:03, 3.40s/ url]
Dl Size...: 37%|███▋ | 550/1501 [00:14<00:32, 29.21 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:14<00:03, 3.40s/ url]
Dl Size...: 37%|███▋ | 551/1501 [00:14<00:32, 29.21 MiB/s]
Dl Size...: 37%|███▋ | 552/1501 [00:14<00:32, 29.10 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:14<00:03, 3.40s/ url]
Dl Size...: 37%|███▋ | 552/1501 [00:14<00:32, 29.10 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:14<00:03, 3.40s/ url]
Dl Size...: 37%|███▋ | 553/1501 [00:14<00:32, 29.10 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:14<00:03, 3.40s/ url]
Dl Size...: 37%|███▋ | 554/1501 [00:14<00:32, 29.10 MiB/s]
Dl Size...: 37%|███▋ | 555/1501 [00:14<00:32, 29.10 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:14<00:03, 3.40s/ url]
Dl Size...: 37%|███▋ | 555/1501 [00:14<00:32, 29.10 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:14<00:03, 3.40s/ url]
Dl Size...: 37%|███▋ | 556/1501 [00:14<00:32, 29.10 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:14<00:03, 3.40s/ url]
Dl Size...: 37%|███▋ | 557/1501 [00:14<00:32, 29.10 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:14<00:03, 3.40s/ url]
Dl Size...: 37%|███▋ | 558/1501 [00:14<00:32, 29.10 MiB/s]
Dl Size...: 37%|███▋ | 559/1501 [00:14<00:32, 29.33 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:14<00:03, 3.40s/ url]
Dl Size...: 37%|███▋ | 559/1501 [00:14<00:32, 29.33 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:14<00:03, 3.40s/ url]
Dl Size...: 37%|███▋ | 560/1501 [00:14<00:32, 29.33 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:14<00:03, 3.40s/ url]
Dl Size...: 37%|███▋ | 561/1501 [00:14<00:32, 29.33 MiB/s]
Dl Size...: 37%|███▋ | 562/1501 [00:14<00:31, 29.35 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:14<00:03, 3.40s/ url]
Dl Size...: 37%|███▋ | 562/1501 [00:14<00:31, 29.35 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:14<00:03, 3.40s/ url]
Dl Size...: 38%|███▊ | 563/1501 [00:14<00:31, 29.35 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:14<00:03, 3.40s/ url]
Dl Size...: 38%|███▊ | 564/1501 [00:14<00:31, 29.35 MiB/s]
Dl Size...: 38%|███▊ | 565/1501 [00:14<00:31, 29.44 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:14<00:03, 3.40s/ url]
Dl Size...: 38%|███▊ | 565/1501 [00:14<00:31, 29.44 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:14<00:03, 3.40s/ url]
Dl Size...: 38%|███▊ | 566/1501 [00:14<00:31, 29.44 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:14<00:03, 3.40s/ url]
Dl Size...: 38%|███▊ | 567/1501 [00:14<00:31, 29.44 MiB/s]
Dl Size...: 38%|███▊ | 568/1501 [00:14<00:31, 29.16 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:14<00:03, 3.40s/ url]
Dl Size...: 38%|███▊ | 568/1501 [00:14<00:31, 29.16 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:14<00:03, 3.40s/ url]
Dl Size...: 38%|███▊ | 569/1501 [00:14<00:31, 29.16 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:14<00:03, 3.40s/ url]
Dl Size...: 38%|███▊ | 570/1501 [00:14<00:31, 29.16 MiB/s]
Dl Size...: 38%|███▊ | 571/1501 [00:14<00:32, 28.79 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:14<00:03, 3.40s/ url]
Dl Size...: 38%|███▊ | 571/1501 [00:14<00:32, 28.79 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:15<00:03, 3.40s/ url]
Dl Size...: 38%|███▊ | 572/1501 [00:15<00:32, 28.79 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:15<00:03, 3.40s/ url]
Dl Size...: 38%|███▊ | 573/1501 [00:15<00:32, 28.79 MiB/s]
Dl Size...: 38%|███▊ | 574/1501 [00:15<00:32, 28.88 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:15<00:03, 3.40s/ url]
Dl Size...: 38%|███▊ | 574/1501 [00:15<00:32, 28.88 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:15<00:03, 3.40s/ url]
Dl Size...: 38%|███▊ | 575/1501 [00:15<00:32, 28.88 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:15<00:03, 3.40s/ url]
Dl Size...: 38%|███▊ | 576/1501 [00:15<00:32, 28.88 MiB/s]
Dl Size...: 38%|███▊ | 577/1501 [00:15<00:33, 27.49 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:15<00:03, 3.40s/ url]
Dl Size...: 38%|███▊ | 577/1501 [00:15<00:33, 27.49 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:15<00:03, 3.40s/ url]
Dl Size...: 39%|███▊ | 578/1501 [00:15<00:33, 27.49 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:15<00:03, 3.40s/ url]
Dl Size...: 39%|███▊ | 579/1501 [00:15<00:33, 27.49 MiB/s]
Dl Size...: 39%|███▊ | 580/1501 [00:15<00:32, 28.02 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:15<00:03, 3.40s/ url]
Dl Size...: 39%|███▊ | 580/1501 [00:15<00:32, 28.02 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:15<00:03, 3.40s/ url]
Dl Size...: 39%|███▊ | 581/1501 [00:15<00:32, 28.02 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:15<00:03, 3.40s/ url]
Dl Size...: 39%|███▉ | 582/1501 [00:15<00:32, 28.02 MiB/s]
Dl Size...: 39%|███▉ | 583/1501 [00:15<00:32, 28.00 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:15<00:03, 3.40s/ url]
Dl Size...: 39%|███▉ | 583/1501 [00:15<00:32, 28.00 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:15<00:03, 3.40s/ url]
Dl Size...: 39%|███▉ | 584/1501 [00:15<00:32, 28.00 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:15<00:03, 3.40s/ url]
Dl Size...: 39%|███▉ | 585/1501 [00:15<00:32, 28.00 MiB/s]
Dl Size...: 39%|███▉ | 586/1501 [00:15<00:32, 28.34 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:15<00:03, 3.40s/ url]
Dl Size...: 39%|███▉ | 586/1501 [00:15<00:32, 28.34 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:15<00:03, 3.40s/ url]
Dl Size...: 39%|███▉ | 587/1501 [00:15<00:32, 28.34 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:15<00:03, 3.40s/ url]
Dl Size...: 39%|███▉ | 588/1501 [00:15<00:32, 28.34 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:15<00:03, 3.40s/ url]
Dl Size...: 39%|███▉ | 589/1501 [00:15<00:32, 28.34 MiB/s]
Dl Size...: 39%|███▉ | 590/1501 [00:15<00:31, 28.95 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:15<00:03, 3.40s/ url]
Dl Size...: 39%|███▉ | 590/1501 [00:15<00:31, 28.95 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:15<00:03, 3.40s/ url]
Dl Size...: 39%|███▉ | 591/1501 [00:15<00:31, 28.95 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:15<00:03, 3.40s/ url]
Dl Size...: 39%|███▉ | 592/1501 [00:15<00:31, 28.95 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:15<00:03, 3.40s/ url]
Dl Size...: 40%|███▉ | 593/1501 [00:15<00:31, 28.95 MiB/s]
Dl Size...: 40%|███▉ | 594/1501 [00:15<00:31, 29.25 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:15<00:03, 3.40s/ url]
Dl Size...: 40%|███▉ | 594/1501 [00:15<00:31, 29.25 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:15<00:03, 3.40s/ url]
Dl Size...: 40%|███▉ | 595/1501 [00:15<00:30, 29.25 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:15<00:03, 3.40s/ url]
Dl Size...: 40%|███▉ | 596/1501 [00:15<00:30, 29.25 MiB/s]
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Dl Completed...: 67%|██████▋ | 2/3 [00:15<00:03, 3.40s/ url]
Dl Size...: 40%|███▉ | 597/1501 [00:15<00:31, 29.08 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:15<00:03, 3.40s/ url]
Dl Size...: 40%|███▉ | 598/1501 [00:15<00:31, 29.08 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:15<00:03, 3.40s/ url]
Dl Size...: 40%|███▉ | 599/1501 [00:15<00:31, 29.08 MiB/s]
Dl Size...: 40%|███▉ | 600/1501 [00:15<00:30, 29.32 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:15<00:03, 3.40s/ url]
Dl Size...: 40%|███▉ | 600/1501 [00:15<00:30, 29.32 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:16<00:03, 3.40s/ url]
Dl Size...: 40%|████ | 601/1501 [00:16<00:30, 29.32 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:16<00:03, 3.40s/ url]
Dl Size...: 40%|████ | 602/1501 [00:16<00:30, 29.32 MiB/s]
Dl Size...: 40%|████ | 603/1501 [00:16<00:30, 29.31 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:16<00:03, 3.40s/ url]
Dl Size...: 40%|████ | 603/1501 [00:16<00:30, 29.31 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:16<00:03, 3.40s/ url]
Dl Size...: 40%|████ | 604/1501 [00:16<00:30, 29.31 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:16<00:03, 3.40s/ url]
Dl Size...: 40%|████ | 605/1501 [00:16<00:30, 29.31 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:16<00:03, 3.40s/ url]
Dl Size...: 40%|████ | 606/1501 [00:16<00:30, 29.31 MiB/s]
Dl Size...: 40%|████ | 607/1501 [00:16<00:30, 29.53 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:16<00:03, 3.40s/ url]
Dl Size...: 40%|████ | 607/1501 [00:16<00:30, 29.53 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:16<00:03, 3.40s/ url]
Dl Size...: 41%|████ | 608/1501 [00:16<00:30, 29.53 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:16<00:03, 3.40s/ url]
Dl Size...: 41%|████ | 609/1501 [00:16<00:30, 29.53 MiB/s]
Dl Size...: 41%|████ | 610/1501 [00:16<00:30, 29.27 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:16<00:03, 3.40s/ url]
Dl Size...: 41%|████ | 610/1501 [00:16<00:30, 29.27 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:16<00:03, 3.40s/ url]
Dl Size...: 41%|████ | 611/1501 [00:16<00:30, 29.27 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:16<00:03, 3.40s/ url]
Dl Size...: 41%|████ | 612/1501 [00:16<00:30, 29.27 MiB/s]
Dl Size...: 41%|████ | 613/1501 [00:16<00:30, 29.18 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:16<00:03, 3.40s/ url]
Dl Size...: 41%|████ | 613/1501 [00:16<00:30, 29.18 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:16<00:03, 3.40s/ url]
Dl Size...: 41%|████ | 614/1501 [00:16<00:30, 29.18 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:16<00:03, 3.40s/ url]
Dl Size...: 41%|████ | 615/1501 [00:16<00:30, 29.18 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:16<00:03, 3.40s/ url]
Dl Size...: 41%|████ | 616/1501 [00:16<00:30, 29.18 MiB/s]
Dl Size...: 41%|████ | 617/1501 [00:16<00:29, 29.59 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:16<00:03, 3.40s/ url]
Dl Size...: 41%|████ | 617/1501 [00:16<00:29, 29.59 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:16<00:03, 3.40s/ url]
Dl Size...: 41%|████ | 618/1501 [00:16<00:29, 29.59 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:16<00:03, 3.40s/ url]
Dl Size...: 41%|████ | 619/1501 [00:16<00:29, 29.59 MiB/s]
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Dl Completed...: 67%|██████▋ | 2/3 [00:16<00:03, 3.40s/ url]
Dl Size...: 41%|████▏ | 620/1501 [00:16<00:29, 29.69 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:16<00:03, 3.40s/ url]
Dl Size...: 41%|████▏ | 621/1501 [00:16<00:29, 29.69 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:16<00:03, 3.40s/ url]
Dl Size...: 41%|████▏ | 622/1501 [00:16<00:29, 29.69 MiB/s]
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Dl Completed...: 67%|██████▋ | 2/3 [00:16<00:03, 3.40s/ url]
Dl Size...: 42%|████▏ | 623/1501 [00:16<00:29, 29.37 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:16<00:03, 3.40s/ url]
Dl Size...: 42%|████▏ | 624/1501 [00:16<00:29, 29.37 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:16<00:03, 3.40s/ url]
Dl Size...: 42%|████▏ | 625/1501 [00:16<00:29, 29.37 MiB/s]
Dl Size...: 42%|████▏ | 626/1501 [00:16<00:29, 29.30 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:16<00:03, 3.40s/ url]
Dl Size...: 42%|████▏ | 626/1501 [00:16<00:29, 29.30 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:16<00:03, 3.40s/ url]
Dl Size...: 42%|████▏ | 627/1501 [00:16<00:29, 29.30 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:16<00:03, 3.40s/ url]
Dl Size...: 42%|████▏ | 628/1501 [00:16<00:29, 29.30 MiB/s]
Dl Size...: 42%|████▏ | 629/1501 [00:16<00:29, 29.29 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:16<00:03, 3.40s/ url]
Dl Size...: 42%|████▏ | 629/1501 [00:16<00:29, 29.29 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:17<00:03, 3.40s/ url]
Dl Size...: 42%|████▏ | 630/1501 [00:17<00:29, 29.29 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:17<00:03, 3.40s/ url]
Dl Size...: 42%|████▏ | 631/1501 [00:17<00:29, 29.29 MiB/s]
Dl Size...: 42%|████▏ | 632/1501 [00:17<00:29, 29.34 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:17<00:03, 3.40s/ url]
Dl Size...: 42%|████▏ | 632/1501 [00:17<00:29, 29.34 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:17<00:03, 3.40s/ url]
Dl Size...: 42%|████▏ | 633/1501 [00:17<00:29, 29.34 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:17<00:03, 3.40s/ url]
Dl Size...: 42%|████▏ | 634/1501 [00:17<00:29, 29.34 MiB/s]
Dl Size...: 42%|████▏ | 635/1501 [00:17<00:29, 29.10 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:17<00:03, 3.40s/ url]
Dl Size...: 42%|████▏ | 635/1501 [00:17<00:29, 29.10 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:17<00:03, 3.40s/ url]
Dl Size...: 42%|████▏ | 636/1501 [00:17<00:29, 29.10 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:17<00:03, 3.40s/ url]
Dl Size...: 42%|████▏ | 637/1501 [00:17<00:29, 29.10 MiB/s]
Dl Size...: 43%|████▎ | 638/1501 [00:17<00:29, 29.00 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:17<00:03, 3.40s/ url]
Dl Size...: 43%|████▎ | 638/1501 [00:17<00:29, 29.00 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:17<00:03, 3.40s/ url]
Dl Size...: 43%|████▎ | 639/1501 [00:17<00:29, 29.00 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:17<00:03, 3.40s/ url]
Dl Size...: 43%|████▎ | 640/1501 [00:17<00:29, 29.00 MiB/s]
Dl Size...: 43%|████▎ | 641/1501 [00:17<00:29, 28.94 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:17<00:03, 3.40s/ url]
Dl Size...: 43%|████▎ | 641/1501 [00:17<00:29, 28.94 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:17<00:03, 3.40s/ url]
Dl Size...: 43%|████▎ | 642/1501 [00:17<00:29, 28.94 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:17<00:03, 3.40s/ url]
Dl Size...: 43%|████▎ | 643/1501 [00:17<00:29, 28.94 MiB/s]
Dl Size...: 43%|████▎ | 644/1501 [00:17<00:29, 28.86 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:17<00:03, 3.40s/ url]
Dl Size...: 43%|████▎ | 644/1501 [00:17<00:29, 28.86 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:17<00:03, 3.40s/ url]
Dl Size...: 43%|████▎ | 645/1501 [00:17<00:29, 28.86 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:17<00:03, 3.40s/ url]
Dl Size...: 43%|████▎ | 646/1501 [00:17<00:29, 28.86 MiB/s]
Dl Size...: 43%|████▎ | 647/1501 [00:17<00:29, 28.75 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:17<00:03, 3.40s/ url]
Dl Size...: 43%|████▎ | 647/1501 [00:17<00:29, 28.75 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:17<00:03, 3.40s/ url]
Dl Size...: 43%|████▎ | 648/1501 [00:17<00:29, 28.75 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:17<00:03, 3.40s/ url]
Dl Size...: 43%|████▎ | 649/1501 [00:17<00:29, 28.75 MiB/s]
Dl Size...: 43%|████▎ | 650/1501 [00:17<00:29, 28.68 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:17<00:03, 3.40s/ url]
Dl Size...: 43%|████▎ | 650/1501 [00:17<00:29, 28.68 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:17<00:03, 3.40s/ url]
Dl Size...: 43%|████▎ | 651/1501 [00:17<00:29, 28.68 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:17<00:03, 3.40s/ url]
Dl Size...: 43%|████▎ | 652/1501 [00:17<00:29, 28.68 MiB/s]
Dl Size...: 44%|████▎ | 653/1501 [00:17<00:29, 28.73 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:17<00:03, 3.40s/ url]
Dl Size...: 44%|████▎ | 653/1501 [00:17<00:29, 28.73 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:17<00:03, 3.40s/ url]
Dl Size...: 44%|████▎ | 654/1501 [00:17<00:29, 28.73 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:17<00:03, 3.40s/ url]
Dl Size...: 44%|████▎ | 655/1501 [00:17<00:29, 28.73 MiB/s]
Dl Size...: 44%|████▎ | 656/1501 [00:17<00:29, 28.64 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:17<00:03, 3.40s/ url]
Dl Size...: 44%|████▎ | 656/1501 [00:17<00:29, 28.64 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:17<00:03, 3.40s/ url]
Dl Size...: 44%|████▍ | 657/1501 [00:17<00:29, 28.64 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:17<00:03, 3.40s/ url]
Dl Size...: 44%|████▍ | 658/1501 [00:17<00:29, 28.64 MiB/s]
Dl Size...: 44%|████▍ | 659/1501 [00:18<00:29, 28.92 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:18<00:03, 3.40s/ url]
Dl Size...: 44%|████▍ | 659/1501 [00:18<00:29, 28.92 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:18<00:03, 3.40s/ url]
Dl Size...: 44%|████▍ | 660/1501 [00:18<00:29, 28.92 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:18<00:03, 3.40s/ url]
Dl Size...: 44%|████▍ | 661/1501 [00:18<00:29, 28.92 MiB/s]
Dl Size...: 44%|████▍ | 662/1501 [00:18<00:29, 28.89 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:18<00:03, 3.40s/ url]
Dl Size...: 44%|████▍ | 662/1501 [00:18<00:29, 28.89 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:18<00:03, 3.40s/ url]
Dl Size...: 44%|████▍ | 663/1501 [00:18<00:29, 28.89 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:18<00:03, 3.40s/ url]
Dl Size...: 44%|████▍ | 664/1501 [00:18<00:28, 28.89 MiB/s]
Dl Size...: 44%|████▍ | 665/1501 [00:18<00:28, 28.97 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:18<00:03, 3.40s/ url]
Dl Size...: 44%|████▍ | 665/1501 [00:18<00:28, 28.97 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:18<00:03, 3.40s/ url]
Dl Size...: 44%|████▍ | 666/1501 [00:18<00:28, 28.97 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:18<00:03, 3.40s/ url]
Dl Size...: 44%|████▍ | 667/1501 [00:18<00:28, 28.97 MiB/s]
Dl Size...: 45%|████▍ | 668/1501 [00:18<00:28, 28.79 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:18<00:03, 3.40s/ url]
Dl Size...: 45%|████▍ | 668/1501 [00:18<00:28, 28.79 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:18<00:03, 3.40s/ url]
Dl Size...: 45%|████▍ | 669/1501 [00:18<00:28, 28.79 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:18<00:03, 3.40s/ url]
Dl Size...: 45%|████▍ | 670/1501 [00:18<00:28, 28.79 MiB/s]
Dl Size...: 45%|████▍ | 671/1501 [00:18<00:28, 29.07 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:18<00:03, 3.40s/ url]
Dl Size...: 45%|████▍ | 671/1501 [00:18<00:28, 29.07 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:18<00:03, 3.40s/ url]
Dl Size...: 45%|████▍ | 672/1501 [00:18<00:28, 29.07 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:18<00:03, 3.40s/ url]
Dl Size...: 45%|████▍ | 673/1501 [00:18<00:28, 29.07 MiB/s]
Dl Size...: 45%|████▍ | 674/1501 [00:18<00:28, 28.93 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:18<00:03, 3.40s/ url]
Dl Size...: 45%|████▍ | 674/1501 [00:18<00:28, 28.93 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:18<00:03, 3.40s/ url]
Dl Size...: 45%|████▍ | 675/1501 [00:18<00:28, 28.93 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:18<00:03, 3.40s/ url]
Dl Size...: 45%|████▌ | 676/1501 [00:18<00:28, 28.93 MiB/s]
Dl Size...: 45%|████▌ | 677/1501 [00:18<00:28, 29.20 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:18<00:03, 3.40s/ url]
Dl Size...: 45%|████▌ | 677/1501 [00:18<00:28, 29.20 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:18<00:03, 3.40s/ url]
Dl Size...: 45%|████▌ | 678/1501 [00:18<00:28, 29.20 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:18<00:03, 3.40s/ url]
Dl Size...: 45%|████▌ | 679/1501 [00:18<00:28, 29.20 MiB/s]
Dl Size...: 45%|████▌ | 680/1501 [00:18<00:28, 28.93 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:18<00:03, 3.40s/ url]
Dl Size...: 45%|████▌ | 680/1501 [00:18<00:28, 28.93 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:18<00:03, 3.40s/ url]
Dl Size...: 45%|████▌ | 681/1501 [00:18<00:28, 28.93 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:18<00:03, 3.40s/ url]
Dl Size...: 45%|████▌ | 682/1501 [00:18<00:28, 28.93 MiB/s]
Dl Size...: 46%|████▌ | 683/1501 [00:18<00:28, 29.13 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:18<00:03, 3.40s/ url]
Dl Size...: 46%|████▌ | 683/1501 [00:18<00:28, 29.13 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:18<00:03, 3.40s/ url]
Dl Size...: 46%|████▌ | 684/1501 [00:18<00:28, 29.13 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:18<00:03, 3.40s/ url]
Dl Size...: 46%|████▌ | 685/1501 [00:18<00:28, 29.13 MiB/s]
Dl Size...: 46%|████▌ | 686/1501 [00:18<00:27, 29.12 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:18<00:03, 3.40s/ url]
Dl Size...: 46%|████▌ | 686/1501 [00:18<00:27, 29.12 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:18<00:03, 3.40s/ url]
Dl Size...: 46%|████▌ | 687/1501 [00:18<00:27, 29.12 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:19<00:03, 3.40s/ url]
Dl Size...: 46%|████▌ | 688/1501 [00:19<00:27, 29.12 MiB/s]
Dl Size...: 46%|████▌ | 689/1501 [00:19<00:27, 29.09 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:19<00:03, 3.40s/ url]
Dl Size...: 46%|████▌ | 689/1501 [00:19<00:27, 29.09 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:19<00:03, 3.40s/ url]
Dl Size...: 46%|████▌ | 690/1501 [00:19<00:27, 29.09 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:19<00:03, 3.40s/ url]
Dl Size...: 46%|████▌ | 691/1501 [00:19<00:27, 29.09 MiB/s]
Dl Size...: 46%|████▌ | 692/1501 [00:19<00:27, 29.10 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:19<00:03, 3.40s/ url]
Dl Size...: 46%|████▌ | 692/1501 [00:19<00:27, 29.10 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:19<00:03, 3.40s/ url]
Dl Size...: 46%|████▌ | 693/1501 [00:19<00:27, 29.10 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:19<00:03, 3.40s/ url]
Dl Size...: 46%|████▌ | 694/1501 [00:19<00:27, 29.10 MiB/s]
Dl Size...: 46%|████▋ | 695/1501 [00:19<00:27, 29.24 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:19<00:03, 3.40s/ url]
Dl Size...: 46%|████▋ | 695/1501 [00:19<00:27, 29.24 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:19<00:03, 3.40s/ url]
Dl Size...: 46%|████▋ | 696/1501 [00:19<00:27, 29.24 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:19<00:03, 3.40s/ url]
Dl Size...: 46%|████▋ | 697/1501 [00:19<00:27, 29.24 MiB/s]
Dl Size...: 47%|████▋ | 698/1501 [00:19<00:27, 29.10 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:19<00:03, 3.40s/ url]
Dl Size...: 47%|████▋ | 698/1501 [00:19<00:27, 29.10 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:19<00:03, 3.40s/ url]
Dl Size...: 47%|████▋ | 699/1501 [00:19<00:27, 29.10 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:19<00:03, 3.40s/ url]
Dl Size...: 47%|████▋ | 700/1501 [00:19<00:27, 29.10 MiB/s]
Dl Size...: 47%|████▋ | 701/1501 [00:19<00:27, 29.06 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:19<00:03, 3.40s/ url]
Dl Size...: 47%|████▋ | 701/1501 [00:19<00:27, 29.06 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:19<00:03, 3.40s/ url]
Dl Size...: 47%|████▋ | 702/1501 [00:19<00:27, 29.06 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:19<00:03, 3.40s/ url]
Dl Size...: 47%|████▋ | 703/1501 [00:19<00:27, 29.06 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:19<00:03, 3.40s/ url]
Dl Size...: 47%|████▋ | 704/1501 [00:19<00:27, 29.06 MiB/s]
Dl Size...: 47%|████▋ | 705/1501 [00:19<00:27, 29.33 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:19<00:03, 3.40s/ url]
Dl Size...: 47%|████▋ | 705/1501 [00:19<00:27, 29.33 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:19<00:03, 3.40s/ url]
Dl Size...: 47%|████▋ | 706/1501 [00:19<00:27, 29.33 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:19<00:03, 3.40s/ url]
Dl Size...: 47%|████▋ | 707/1501 [00:19<00:27, 29.33 MiB/s]
Dl Size...: 47%|████▋ | 708/1501 [00:19<00:26, 29.43 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:19<00:03, 3.40s/ url]
Dl Size...: 47%|████▋ | 708/1501 [00:19<00:26, 29.43 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:19<00:03, 3.40s/ url]
Dl Size...: 47%|████▋ | 709/1501 [00:19<00:26, 29.43 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:19<00:03, 3.40s/ url]
Dl Size...: 47%|████▋ | 710/1501 [00:19<00:26, 29.43 MiB/s]
Dl Size...: 47%|████▋ | 711/1501 [00:19<00:27, 29.26 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:19<00:03, 3.40s/ url]
Dl Size...: 47%|████▋ | 711/1501 [00:19<00:27, 29.26 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:19<00:03, 3.40s/ url]
Dl Size...: 47%|████▋ | 712/1501 [00:19<00:26, 29.26 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:19<00:03, 3.40s/ url]
Dl Size...: 48%|████▊ | 713/1501 [00:19<00:26, 29.26 MiB/s]
Dl Size...: 48%|████▊ | 714/1501 [00:19<00:26, 29.19 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:19<00:03, 3.40s/ url]
Dl Size...: 48%|████▊ | 714/1501 [00:19<00:26, 29.19 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:19<00:03, 3.40s/ url]
Dl Size...: 48%|████▊ | 715/1501 [00:19<00:26, 29.19 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:19<00:03, 3.40s/ url]
Dl Size...: 48%|████▊ | 716/1501 [00:19<00:26, 29.19 MiB/s]
Dl Size...: 48%|████▊ | 717/1501 [00:20<00:26, 29.35 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:20<00:03, 3.40s/ url]
Dl Size...: 48%|████▊ | 717/1501 [00:20<00:26, 29.35 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:20<00:03, 3.40s/ url]
Dl Size...: 48%|████▊ | 718/1501 [00:20<00:26, 29.35 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:20<00:03, 3.40s/ url]
Dl Size...: 48%|████▊ | 719/1501 [00:20<00:26, 29.35 MiB/s]
Dl Size...: 48%|████▊ | 720/1501 [00:20<00:26, 29.00 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:20<00:03, 3.40s/ url]
Dl Size...: 48%|████▊ | 720/1501 [00:20<00:26, 29.00 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:20<00:03, 3.40s/ url]
Dl Size...: 48%|████▊ | 721/1501 [00:20<00:26, 29.00 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:20<00:03, 3.40s/ url]
Dl Size...: 48%|████▊ | 722/1501 [00:20<00:26, 29.00 MiB/s]
Dl Size...: 48%|████▊ | 723/1501 [00:20<00:26, 28.96 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:20<00:03, 3.40s/ url]
Dl Size...: 48%|████▊ | 723/1501 [00:20<00:26, 28.96 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:20<00:03, 3.40s/ url]
Dl Size...: 48%|████▊ | 724/1501 [00:20<00:26, 28.96 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:20<00:03, 3.40s/ url]
Dl Size...: 48%|████▊ | 725/1501 [00:20<00:26, 28.96 MiB/s]
Dl Size...: 48%|████▊ | 726/1501 [00:20<00:26, 28.78 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:20<00:03, 3.40s/ url]
Dl Size...: 48%|████▊ | 726/1501 [00:20<00:26, 28.78 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:20<00:03, 3.40s/ url]
Dl Size...: 48%|████▊ | 727/1501 [00:20<00:26, 28.78 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:20<00:03, 3.40s/ url]
Dl Size...: 49%|████▊ | 728/1501 [00:20<00:26, 28.78 MiB/s]
Dl Size...: 49%|████▊ | 729/1501 [00:20<00:26, 28.83 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:20<00:03, 3.40s/ url]
Dl Size...: 49%|████▊ | 729/1501 [00:20<00:26, 28.83 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:20<00:03, 3.40s/ url]
Dl Size...: 49%|████▊ | 730/1501 [00:20<00:26, 28.83 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:20<00:03, 3.40s/ url]
Dl Size...: 49%|████▊ | 731/1501 [00:20<00:26, 28.83 MiB/s]
Dl Size...: 49%|████▉ | 732/1501 [00:20<00:27, 28.14 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:20<00:03, 3.40s/ url]
Dl Size...: 49%|████▉ | 732/1501 [00:20<00:27, 28.14 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:20<00:03, 3.40s/ url]
Dl Size...: 49%|████▉ | 733/1501 [00:20<00:27, 28.14 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:20<00:03, 3.40s/ url]
Dl Size...: 49%|████▉ | 734/1501 [00:20<00:27, 28.14 MiB/s]
Dl Size...: 49%|████▉ | 735/1501 [00:20<00:28, 26.50 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:20<00:03, 3.40s/ url]
Dl Size...: 49%|████▉ | 735/1501 [00:20<00:28, 26.50 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:20<00:03, 3.40s/ url]
Dl Size...: 49%|████▉ | 736/1501 [00:20<00:28, 26.50 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:20<00:03, 3.40s/ url]
Dl Size...: 49%|████▉ | 737/1501 [00:20<00:28, 26.50 MiB/s]
Dl Size...: 49%|████▉ | 738/1501 [00:20<00:27, 27.42 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:20<00:03, 3.40s/ url]
Dl Size...: 49%|████▉ | 738/1501 [00:20<00:27, 27.42 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:20<00:03, 3.40s/ url]
Dl Size...: 49%|████▉ | 739/1501 [00:20<00:27, 27.42 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:20<00:03, 3.40s/ url]
Dl Size...: 49%|████▉ | 740/1501 [00:20<00:27, 27.42 MiB/s]
Dl Size...: 49%|████▉ | 741/1501 [00:20<00:27, 27.98 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:20<00:03, 3.40s/ url]
Dl Size...: 49%|████▉ | 741/1501 [00:20<00:27, 27.98 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:20<00:03, 3.40s/ url]
Dl Size...: 49%|████▉ | 742/1501 [00:20<00:27, 27.98 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:20<00:03, 3.40s/ url]
Dl Size...: 50%|████▉ | 743/1501 [00:20<00:27, 27.98 MiB/s]
Dl Size...: 50%|████▉ | 744/1501 [00:20<00:26, 28.23 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:20<00:03, 3.40s/ url]
Dl Size...: 50%|████▉ | 744/1501 [00:20<00:26, 28.23 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:21<00:03, 3.40s/ url]
Dl Size...: 50%|████▉ | 745/1501 [00:21<00:26, 28.23 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:21<00:03, 3.40s/ url]
Dl Size...: 50%|████▉ | 746/1501 [00:21<00:26, 28.23 MiB/s]
Dl Size...: 50%|████▉ | 747/1501 [00:21<00:26, 28.31 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:21<00:03, 3.40s/ url]
Dl Size...: 50%|████▉ | 747/1501 [00:21<00:26, 28.31 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:21<00:03, 3.40s/ url]
Dl Size...: 50%|████▉ | 748/1501 [00:21<00:26, 28.31 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:21<00:03, 3.40s/ url]
Dl Size...: 50%|████▉ | 749/1501 [00:21<00:26, 28.31 MiB/s]
Dl Size...: 50%|████▉ | 750/1501 [00:21<00:26, 28.54 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:21<00:03, 3.40s/ url]
Dl Size...: 50%|████▉ | 750/1501 [00:21<00:26, 28.54 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:21<00:03, 3.40s/ url]
Dl Size...: 50%|█████ | 751/1501 [00:21<00:26, 28.54 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:21<00:03, 3.40s/ url]
Dl Size...: 50%|█████ | 752/1501 [00:21<00:26, 28.54 MiB/s]
Dl Size...: 50%|█████ | 753/1501 [00:21<00:25, 28.87 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:21<00:03, 3.40s/ url]
Dl Size...: 50%|█████ | 753/1501 [00:21<00:25, 28.87 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:21<00:03, 3.40s/ url]
Dl Size...: 50%|█████ | 754/1501 [00:21<00:25, 28.87 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:21<00:03, 3.40s/ url]
Dl Size...: 50%|█████ | 755/1501 [00:21<00:25, 28.87 MiB/s]
Dl Size...: 50%|█████ | 756/1501 [00:21<00:25, 28.69 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:21<00:03, 3.40s/ url]
Dl Size...: 50%|█████ | 756/1501 [00:21<00:25, 28.69 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:21<00:03, 3.40s/ url]
Dl Size...: 50%|█████ | 757/1501 [00:21<00:25, 28.69 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:21<00:03, 3.40s/ url]
Dl Size...: 50%|█████ | 758/1501 [00:21<00:25, 28.69 MiB/s]
Dl Size...: 51%|█████ | 759/1501 [00:21<00:25, 28.66 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:21<00:03, 3.40s/ url]
Dl Size...: 51%|█████ | 759/1501 [00:21<00:25, 28.66 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:21<00:03, 3.40s/ url]
Dl Size...: 51%|█████ | 760/1501 [00:21<00:25, 28.66 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:21<00:03, 3.40s/ url]
Dl Size...: 51%|█████ | 761/1501 [00:21<00:25, 28.66 MiB/s]
Dl Size...: 51%|█████ | 762/1501 [00:21<00:25, 29.01 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:21<00:03, 3.40s/ url]
Dl Size...: 51%|█████ | 762/1501 [00:21<00:25, 29.01 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:21<00:03, 3.40s/ url]
Dl Size...: 51%|█████ | 763/1501 [00:21<00:25, 29.01 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:21<00:03, 3.40s/ url]
Dl Size...: 51%|█████ | 764/1501 [00:21<00:25, 29.01 MiB/s]
Dl Size...: 51%|█████ | 765/1501 [00:21<00:25, 29.25 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:21<00:03, 3.40s/ url]
Dl Size...: 51%|█████ | 765/1501 [00:21<00:25, 29.25 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:21<00:03, 3.40s/ url]
Dl Size...: 51%|█████ | 766/1501 [00:21<00:25, 29.25 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:21<00:03, 3.40s/ url]
Dl Size...: 51%|█████ | 767/1501 [00:21<00:25, 29.25 MiB/s]
Dl Size...: 51%|█████ | 768/1501 [00:21<00:25, 29.23 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:21<00:03, 3.40s/ url]
Dl Size...: 51%|█████ | 768/1501 [00:21<00:25, 29.23 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:21<00:03, 3.40s/ url]
Dl Size...: 51%|█████ | 769/1501 [00:21<00:25, 29.23 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:21<00:03, 3.40s/ url]
Dl Size...: 51%|█████▏ | 770/1501 [00:21<00:25, 29.23 MiB/s]
Dl Size...: 51%|█████▏ | 771/1501 [00:21<00:24, 29.37 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:21<00:03, 3.40s/ url]
Dl Size...: 51%|█████▏ | 771/1501 [00:21<00:24, 29.37 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:21<00:03, 3.40s/ url]
Dl Size...: 51%|█████▏ | 772/1501 [00:21<00:24, 29.37 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:21<00:03, 3.40s/ url]
Dl Size...: 51%|█████▏ | 773/1501 [00:21<00:24, 29.37 MiB/s]
Dl Size...: 52%|█████▏ | 774/1501 [00:22<00:24, 29.47 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:22<00:03, 3.40s/ url]
Dl Size...: 52%|█████▏ | 774/1501 [00:22<00:24, 29.47 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:22<00:03, 3.40s/ url]
Dl Size...: 52%|█████▏ | 775/1501 [00:22<00:24, 29.47 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:22<00:03, 3.40s/ url]
Dl Size...: 52%|█████▏ | 776/1501 [00:22<00:24, 29.47 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:22<00:03, 3.40s/ url]
Dl Size...: 52%|█████▏ | 777/1501 [00:22<00:24, 29.47 MiB/s]
Dl Size...: 52%|█████▏ | 778/1501 [00:22<00:24, 29.51 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:22<00:03, 3.40s/ url]
Dl Size...: 52%|█████▏ | 778/1501 [00:22<00:24, 29.51 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:22<00:03, 3.40s/ url]
Dl Size...: 52%|█████▏ | 779/1501 [00:22<00:24, 29.51 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:22<00:03, 3.40s/ url]
Dl Size...: 52%|█████▏ | 780/1501 [00:22<00:24, 29.51 MiB/s]
Dl Size...: 52%|█████▏ | 781/1501 [00:22<00:24, 29.13 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:22<00:03, 3.40s/ url]
Dl Size...: 52%|█████▏ | 781/1501 [00:22<00:24, 29.13 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:22<00:03, 3.40s/ url]
Dl Size...: 52%|█████▏ | 782/1501 [00:22<00:24, 29.13 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:22<00:03, 3.40s/ url]
Dl Size...: 52%|█████▏ | 783/1501 [00:22<00:24, 29.13 MiB/s]
Dl Size...: 52%|█████▏ | 784/1501 [00:22<00:24, 29.26 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:22<00:03, 3.40s/ url]
Dl Size...: 52%|█████▏ | 784/1501 [00:22<00:24, 29.26 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:22<00:03, 3.40s/ url]
Dl Size...: 52%|█████▏ | 785/1501 [00:22<00:24, 29.26 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:22<00:03, 3.40s/ url]
Dl Size...: 52%|█████▏ | 786/1501 [00:22<00:24, 29.26 MiB/s]
Dl Size...: 52%|█████▏ | 787/1501 [00:22<00:24, 29.36 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:22<00:03, 3.40s/ url]
Dl Size...: 52%|█████▏ | 787/1501 [00:22<00:24, 29.36 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:22<00:03, 3.40s/ url]
Dl Size...: 52%|█████▏ | 788/1501 [00:22<00:24, 29.36 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:22<00:03, 3.40s/ url]
Dl Size...: 53%|█████▎ | 789/1501 [00:22<00:24, 29.36 MiB/s]
Dl Size...: 53%|█████▎ | 790/1501 [00:22<00:24, 29.41 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:22<00:03, 3.40s/ url]
Dl Size...: 53%|█████▎ | 790/1501 [00:22<00:24, 29.41 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:22<00:03, 3.40s/ url]
Dl Size...: 53%|█████▎ | 791/1501 [00:22<00:24, 29.41 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:22<00:03, 3.40s/ url]
Dl Size...: 53%|█████▎ | 792/1501 [00:22<00:24, 29.41 MiB/s]
Dl Size...: 53%|█████▎ | 793/1501 [00:22<00:24, 29.17 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:22<00:03, 3.40s/ url]
Dl Size...: 53%|█████▎ | 793/1501 [00:22<00:24, 29.17 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:22<00:03, 3.40s/ url]
Dl Size...: 53%|█████▎ | 794/1501 [00:22<00:24, 29.17 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:22<00:03, 3.40s/ url]
Dl Size...: 53%|█████▎ | 795/1501 [00:22<00:24, 29.17 MiB/s]
Dl Size...: 53%|█████▎ | 796/1501 [00:22<00:24, 29.20 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:22<00:03, 3.40s/ url]
Dl Size...: 53%|█████▎ | 796/1501 [00:22<00:24, 29.20 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:22<00:03, 3.40s/ url]
Dl Size...: 53%|█████▎ | 797/1501 [00:22<00:24, 29.20 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:22<00:03, 3.40s/ url]
Dl Size...: 53%|█████▎ | 798/1501 [00:22<00:24, 29.20 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:22<00:03, 3.40s/ url]
Dl Size...: 53%|█████▎ | 799/1501 [00:22<00:24, 29.20 MiB/s]
Dl Size...: 53%|█████▎ | 800/1501 [00:22<00:23, 29.50 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:22<00:03, 3.40s/ url]
Dl Size...: 53%|█████▎ | 800/1501 [00:22<00:23, 29.50 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:22<00:03, 3.40s/ url]
Dl Size...: 53%|█████▎ | 801/1501 [00:22<00:23, 29.50 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:22<00:03, 3.40s/ url]
Dl Size...: 53%|█████▎ | 802/1501 [00:22<00:23, 29.50 MiB/s]
Dl Size...: 53%|█████▎ | 803/1501 [00:22<00:23, 29.25 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:22<00:03, 3.40s/ url]
Dl Size...: 53%|█████▎ | 803/1501 [00:22<00:23, 29.25 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:23<00:03, 3.40s/ url]
Dl Size...: 54%|█████▎ | 804/1501 [00:23<00:23, 29.25 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:23<00:03, 3.40s/ url]
Dl Size...: 54%|█████▎ | 805/1501 [00:23<00:23, 29.25 MiB/s]
Dl Size...: 54%|█████▎ | 806/1501 [00:23<00:23, 29.28 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:23<00:03, 3.40s/ url]
Dl Size...: 54%|█████▎ | 806/1501 [00:23<00:23, 29.28 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:23<00:03, 3.40s/ url]
Dl Size...: 54%|█████▍ | 807/1501 [00:23<00:23, 29.28 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:23<00:03, 3.40s/ url]
Dl Size...: 54%|█████▍ | 808/1501 [00:23<00:23, 29.28 MiB/s]
Dl Size...: 54%|█████▍ | 809/1501 [00:23<00:23, 29.43 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:23<00:03, 3.40s/ url]
Dl Size...: 54%|█████▍ | 809/1501 [00:23<00:23, 29.43 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:23<00:03, 3.40s/ url]
Dl Size...: 54%|█████▍ | 810/1501 [00:23<00:23, 29.43 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:23<00:03, 3.40s/ url]
Dl Size...: 54%|█████▍ | 811/1501 [00:23<00:23, 29.43 MiB/s]
Dl Size...: 54%|█████▍ | 812/1501 [00:23<00:23, 29.15 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:23<00:03, 3.40s/ url]
Dl Size...: 54%|█████▍ | 812/1501 [00:23<00:23, 29.15 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:23<00:03, 3.40s/ url]
Dl Size...: 54%|█████▍ | 813/1501 [00:23<00:23, 29.15 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:23<00:03, 3.40s/ url]
Dl Size...: 54%|█████▍ | 814/1501 [00:23<00:23, 29.15 MiB/s]
Dl Size...: 54%|█████▍ | 815/1501 [00:23<00:23, 29.11 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:23<00:03, 3.40s/ url]
Dl Size...: 54%|█████▍ | 815/1501 [00:23<00:23, 29.11 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:23<00:03, 3.40s/ url]
Dl Size...: 54%|█████▍ | 816/1501 [00:23<00:23, 29.11 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:23<00:03, 3.40s/ url]
Dl Size...: 54%|█████▍ | 817/1501 [00:23<00:23, 29.11 MiB/s]
Dl Size...: 54%|█████▍ | 818/1501 [00:23<00:23, 28.96 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:23<00:03, 3.40s/ url]
Dl Size...: 54%|█████▍ | 818/1501 [00:23<00:23, 28.96 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:23<00:03, 3.40s/ url]
Dl Size...: 55%|█████▍ | 819/1501 [00:23<00:23, 28.96 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:23<00:03, 3.40s/ url]
Dl Size...: 55%|█████▍ | 820/1501 [00:23<00:23, 28.96 MiB/s]
Dl Size...: 55%|█████▍ | 821/1501 [00:23<00:23, 29.06 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:23<00:03, 3.40s/ url]
Dl Size...: 55%|█████▍ | 821/1501 [00:23<00:23, 29.06 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:23<00:03, 3.40s/ url]
Dl Size...: 55%|█████▍ | 822/1501 [00:23<00:23, 29.06 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:23<00:03, 3.40s/ url]
Dl Size...: 55%|█████▍ | 823/1501 [00:23<00:23, 29.06 MiB/s]
Dl Size...: 55%|█████▍ | 824/1501 [00:23<00:23, 28.78 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:23<00:03, 3.40s/ url]
Dl Size...: 55%|█████▍ | 824/1501 [00:23<00:23, 28.78 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:23<00:03, 3.40s/ url]
Dl Size...: 55%|█████▍ | 825/1501 [00:23<00:23, 28.78 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:23<00:03, 3.40s/ url]
Dl Size...: 55%|█████▌ | 826/1501 [00:23<00:23, 28.78 MiB/s]
Dl Size...: 55%|█████▌ | 827/1501 [00:23<00:23, 28.77 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:23<00:03, 3.40s/ url]
Dl Size...: 55%|█████▌ | 827/1501 [00:23<00:23, 28.77 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:23<00:03, 3.40s/ url]
Dl Size...: 55%|█████▌ | 828/1501 [00:23<00:23, 28.77 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:23<00:03, 3.40s/ url]
Dl Size...: 55%|█████▌ | 829/1501 [00:23<00:23, 28.77 MiB/s]
Dl Size...: 55%|█████▌ | 830/1501 [00:23<00:23, 28.65 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:23<00:03, 3.40s/ url]
Dl Size...: 55%|█████▌ | 830/1501 [00:23<00:23, 28.65 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:23<00:03, 3.40s/ url]
Dl Size...: 55%|█████▌ | 831/1501 [00:23<00:23, 28.65 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:23<00:03, 3.40s/ url]
Dl Size...: 55%|█████▌ | 832/1501 [00:23<00:23, 28.65 MiB/s]
Dl Size...: 55%|█████▌ | 833/1501 [00:24<00:23, 28.89 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:24<00:03, 3.40s/ url]
Dl Size...: 55%|█████▌ | 833/1501 [00:24<00:23, 28.89 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:24<00:03, 3.40s/ url]
Dl Size...: 56%|█████▌ | 834/1501 [00:24<00:23, 28.89 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:24<00:03, 3.40s/ url]
Dl Size...: 56%|█████▌ | 835/1501 [00:24<00:23, 28.89 MiB/s]
Dl Size...: 56%|█████▌ | 836/1501 [00:24<00:22, 29.04 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:24<00:03, 3.40s/ url]
Dl Size...: 56%|█████▌ | 836/1501 [00:24<00:22, 29.04 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:24<00:03, 3.40s/ url]
Dl Size...: 56%|█████▌ | 837/1501 [00:24<00:22, 29.04 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:24<00:03, 3.40s/ url]
Dl Size...: 56%|█████▌ | 838/1501 [00:24<00:22, 29.04 MiB/s]
Dl Size...: 56%|█████▌ | 839/1501 [00:24<00:23, 28.57 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:24<00:03, 3.40s/ url]
Dl Size...: 56%|█████▌ | 839/1501 [00:24<00:23, 28.57 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:24<00:03, 3.40s/ url]
Dl Size...: 56%|█████▌ | 840/1501 [00:24<00:23, 28.57 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:24<00:03, 3.40s/ url]
Dl Size...: 56%|█████▌ | 841/1501 [00:24<00:23, 28.57 MiB/s]
Dl Size...: 56%|█████▌ | 842/1501 [00:24<00:22, 28.76 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:24<00:03, 3.40s/ url]
Dl Size...: 56%|█████▌ | 842/1501 [00:24<00:22, 28.76 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:24<00:03, 3.40s/ url]
Dl Size...: 56%|█████▌ | 843/1501 [00:24<00:22, 28.76 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:24<00:03, 3.40s/ url]
Dl Size...: 56%|█████▌ | 844/1501 [00:24<00:22, 28.76 MiB/s]
Dl Size...: 56%|█████▋ | 845/1501 [00:24<00:22, 28.89 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:24<00:03, 3.40s/ url]
Dl Size...: 56%|█████▋ | 845/1501 [00:24<00:22, 28.89 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:24<00:03, 3.40s/ url]
Dl Size...: 56%|█████▋ | 846/1501 [00:24<00:22, 28.89 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:24<00:03, 3.40s/ url]
Dl Size...: 56%|█████▋ | 847/1501 [00:24<00:22, 28.89 MiB/s]
Dl Size...: 56%|█████▋ | 848/1501 [00:24<00:22, 28.64 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:24<00:03, 3.40s/ url]
Dl Size...: 56%|█████▋ | 848/1501 [00:24<00:22, 28.64 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:24<00:03, 3.40s/ url]
Dl Size...: 57%|█████▋ | 849/1501 [00:24<00:22, 28.64 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:24<00:03, 3.40s/ url]
Dl Size...: 57%|█████▋ | 850/1501 [00:24<00:22, 28.64 MiB/s]
Dl Size...: 57%|█████▋ | 851/1501 [00:24<00:22, 28.56 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:24<00:03, 3.40s/ url]
Dl Size...: 57%|█████▋ | 851/1501 [00:24<00:22, 28.56 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:24<00:03, 3.40s/ url]
Dl Size...: 57%|█████▋ | 852/1501 [00:24<00:22, 28.56 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:24<00:03, 3.40s/ url]
Dl Size...: 57%|█████▋ | 853/1501 [00:24<00:22, 28.56 MiB/s]
Dl Size...: 57%|█████▋ | 854/1501 [00:24<00:22, 28.83 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:24<00:03, 3.40s/ url]
Dl Size...: 57%|█████▋ | 854/1501 [00:24<00:22, 28.83 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:24<00:03, 3.40s/ url]
Dl Size...: 57%|█████▋ | 855/1501 [00:24<00:22, 28.83 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:24<00:03, 3.40s/ url]
Dl Size...: 57%|█████▋ | 856/1501 [00:24<00:22, 28.83 MiB/s]
Dl Size...: 57%|█████▋ | 857/1501 [00:24<00:22, 29.09 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:24<00:03, 3.40s/ url]
Dl Size...: 57%|█████▋ | 857/1501 [00:24<00:22, 29.09 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:24<00:03, 3.40s/ url]
Dl Size...: 57%|█████▋ | 858/1501 [00:24<00:22, 29.09 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:24<00:03, 3.40s/ url]
Dl Size...: 57%|█████▋ | 859/1501 [00:24<00:22, 29.09 MiB/s]
Dl Size...: 57%|█████▋ | 860/1501 [00:24<00:22, 28.79 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:24<00:03, 3.40s/ url]
Dl Size...: 57%|█████▋ | 860/1501 [00:24<00:22, 28.79 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:25<00:03, 3.40s/ url]
Dl Size...: 57%|█████▋ | 861/1501 [00:25<00:22, 28.79 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:25<00:03, 3.40s/ url]
Dl Size...: 57%|█████▋ | 862/1501 [00:25<00:22, 28.79 MiB/s]
Dl Size...: 57%|█████▋ | 863/1501 [00:25<00:22, 28.76 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:25<00:03, 3.40s/ url]
Dl Size...: 57%|█████▋ | 863/1501 [00:25<00:22, 28.76 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:25<00:03, 3.40s/ url]
Dl Size...: 58%|█████▊ | 864/1501 [00:25<00:22, 28.76 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:25<00:03, 3.40s/ url]
Dl Size...: 58%|█████▊ | 865/1501 [00:25<00:22, 28.76 MiB/s]
Dl Size...: 58%|█████▊ | 866/1501 [00:25<00:22, 28.52 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:25<00:03, 3.40s/ url]
Dl Size...: 58%|█████▊ | 866/1501 [00:25<00:22, 28.52 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:25<00:03, 3.40s/ url]
Dl Size...: 58%|█████▊ | 867/1501 [00:25<00:22, 28.52 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:25<00:03, 3.40s/ url]
Dl Size...: 58%|█████▊ | 868/1501 [00:25<00:22, 28.52 MiB/s]
Dl Size...: 58%|█████▊ | 869/1501 [00:25<00:22, 28.46 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:25<00:03, 3.40s/ url]
Dl Size...: 58%|█████▊ | 869/1501 [00:25<00:22, 28.46 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:25<00:03, 3.40s/ url]
Dl Size...: 58%|█████▊ | 870/1501 [00:25<00:22, 28.46 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:25<00:03, 3.40s/ url]
Dl Size...: 58%|█████▊ | 871/1501 [00:25<00:22, 28.46 MiB/s]
Dl Size...: 58%|█████▊ | 872/1501 [00:25<00:22, 28.46 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:25<00:03, 3.40s/ url]
Dl Size...: 58%|█████▊ | 872/1501 [00:25<00:22, 28.46 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:25<00:03, 3.40s/ url]
Dl Size...: 58%|█████▊ | 873/1501 [00:25<00:22, 28.46 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:25<00:03, 3.40s/ url]
Dl Size...: 58%|█████▊ | 874/1501 [00:25<00:22, 28.46 MiB/s]
Dl Size...: 58%|█████▊ | 875/1501 [00:25<00:22, 28.23 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:25<00:03, 3.40s/ url]
Dl Size...: 58%|█████▊ | 875/1501 [00:25<00:22, 28.23 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:25<00:03, 3.40s/ url]
Dl Size...: 58%|█████▊ | 876/1501 [00:25<00:22, 28.23 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:25<00:03, 3.40s/ url]
Dl Size...: 58%|█████▊ | 877/1501 [00:25<00:22, 28.23 MiB/s]
Dl Size...: 58%|█████▊ | 878/1501 [00:25<00:21, 28.41 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:25<00:03, 3.40s/ url]
Dl Size...: 58%|█████▊ | 878/1501 [00:25<00:21, 28.41 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:25<00:03, 3.40s/ url]
Dl Size...: 59%|█████▊ | 879/1501 [00:25<00:21, 28.41 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:25<00:03, 3.40s/ url]
Dl Size...: 59%|█████▊ | 880/1501 [00:25<00:21, 28.41 MiB/s]
Dl Size...: 59%|█████▊ | 881/1501 [00:25<00:21, 28.52 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:25<00:03, 3.40s/ url]
Dl Size...: 59%|█████▊ | 881/1501 [00:25<00:21, 28.52 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:25<00:03, 3.40s/ url]
Dl Size...: 59%|█████▉ | 882/1501 [00:25<00:21, 28.52 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:25<00:03, 3.40s/ url]
Dl Size...: 59%|█████▉ | 883/1501 [00:25<00:21, 28.52 MiB/s]
Dl Size...: 59%|█████▉ | 884/1501 [00:25<00:21, 28.61 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:25<00:03, 3.40s/ url]
Dl Size...: 59%|█████▉ | 884/1501 [00:25<00:21, 28.61 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:25<00:03, 3.40s/ url]
Dl Size...: 59%|█████▉ | 885/1501 [00:25<00:21, 28.61 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:25<00:03, 3.40s/ url]
Dl Size...: 59%|█████▉ | 886/1501 [00:25<00:21, 28.61 MiB/s]
Dl Size...: 59%|█████▉ | 887/1501 [00:25<00:21, 28.43 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:25<00:03, 3.40s/ url]
Dl Size...: 59%|█████▉ | 887/1501 [00:25<00:21, 28.43 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:25<00:03, 3.40s/ url]
Dl Size...: 59%|█████▉ | 888/1501 [00:25<00:21, 28.43 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:25<00:03, 3.40s/ url]
Dl Size...: 59%|█████▉ | 889/1501 [00:25<00:21, 28.43 MiB/s]
Dl Size...: 59%|█████▉ | 890/1501 [00:26<00:21, 28.67 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:26<00:03, 3.40s/ url]
Dl Size...: 59%|█████▉ | 890/1501 [00:26<00:21, 28.67 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:26<00:03, 3.40s/ url]
Dl Size...: 59%|█████▉ | 891/1501 [00:26<00:21, 28.67 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:26<00:03, 3.40s/ url]
Dl Size...: 59%|█████▉ | 892/1501 [00:26<00:21, 28.67 MiB/s]
Dl Size...: 59%|█████▉ | 893/1501 [00:26<00:21, 28.71 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:26<00:03, 3.40s/ url]
Dl Size...: 59%|█████▉ | 893/1501 [00:26<00:21, 28.71 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:26<00:03, 3.40s/ url]
Dl Size...: 60%|█████▉ | 894/1501 [00:26<00:21, 28.71 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:26<00:03, 3.40s/ url]
Dl Size...: 60%|█████▉ | 895/1501 [00:26<00:21, 28.71 MiB/s]
Dl Size...: 60%|█████▉ | 896/1501 [00:26<00:21, 28.80 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:26<00:03, 3.40s/ url]
Dl Size...: 60%|█████▉ | 896/1501 [00:26<00:21, 28.80 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:26<00:03, 3.40s/ url]
Dl Size...: 60%|█████▉ | 897/1501 [00:26<00:20, 28.80 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:26<00:03, 3.40s/ url]
Dl Size...: 60%|█████▉ | 898/1501 [00:26<00:20, 28.80 MiB/s]
Dl Size...: 60%|█████▉ | 899/1501 [00:26<00:20, 28.82 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:26<00:03, 3.40s/ url]
Dl Size...: 60%|█████▉ | 899/1501 [00:26<00:20, 28.82 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:26<00:03, 3.40s/ url]
Dl Size...: 60%|█████▉ | 900/1501 [00:26<00:20, 28.82 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:26<00:03, 3.40s/ url]
Dl Size...: 60%|██████ | 901/1501 [00:26<00:20, 28.82 MiB/s]
Dl Size...: 60%|██████ | 902/1501 [00:26<00:20, 28.68 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:26<00:03, 3.40s/ url]
Dl Size...: 60%|██████ | 902/1501 [00:26<00:20, 28.68 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:26<00:03, 3.40s/ url]
Dl Size...: 60%|██████ | 903/1501 [00:26<00:20, 28.68 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:26<00:03, 3.40s/ url]
Dl Size...: 60%|██████ | 904/1501 [00:26<00:20, 28.68 MiB/s]
Dl Size...: 60%|██████ | 905/1501 [00:26<00:21, 28.30 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:26<00:03, 3.40s/ url]
Dl Size...: 60%|██████ | 905/1501 [00:26<00:21, 28.30 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:26<00:03, 3.40s/ url]
Dl Size...: 60%|██████ | 906/1501 [00:26<00:21, 28.30 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:26<00:03, 3.40s/ url]
Dl Size...: 60%|██████ | 907/1501 [00:26<00:20, 28.30 MiB/s]
Dl Size...: 60%|██████ | 908/1501 [00:26<00:20, 28.44 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:26<00:03, 3.40s/ url]
Dl Size...: 60%|██████ | 908/1501 [00:26<00:20, 28.44 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:26<00:03, 3.40s/ url]
Dl Size...: 61%|██████ | 909/1501 [00:26<00:20, 28.44 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:26<00:03, 3.40s/ url]
Dl Size...: 61%|██████ | 910/1501 [00:26<00:20, 28.44 MiB/s]
Dl Size...: 61%|██████ | 911/1501 [00:26<00:20, 28.67 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:26<00:03, 3.40s/ url]
Dl Size...: 61%|██████ | 911/1501 [00:26<00:20, 28.67 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:26<00:03, 3.40s/ url]
Dl Size...: 61%|██████ | 912/1501 [00:26<00:20, 28.67 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:26<00:03, 3.40s/ url]
Dl Size...: 61%|██████ | 913/1501 [00:26<00:20, 28.67 MiB/s]
Dl Size...: 61%|██████ | 914/1501 [00:26<00:20, 28.82 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:26<00:03, 3.40s/ url]
Dl Size...: 61%|██████ | 914/1501 [00:26<00:20, 28.82 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:26<00:03, 3.40s/ url]
Dl Size...: 61%|██████ | 915/1501 [00:26<00:20, 28.82 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:26<00:03, 3.40s/ url]
Dl Size...: 61%|██████ | 916/1501 [00:26<00:20, 28.82 MiB/s]
Dl Size...: 61%|██████ | 917/1501 [00:26<00:20, 28.39 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:26<00:03, 3.40s/ url]
Dl Size...: 61%|██████ | 917/1501 [00:26<00:20, 28.39 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:26<00:03, 3.40s/ url]
Dl Size...: 61%|██████ | 918/1501 [00:26<00:20, 28.39 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:27<00:03, 3.40s/ url]
Dl Size...: 61%|██████ | 919/1501 [00:27<00:20, 28.39 MiB/s]
Dl Size...: 61%|██████▏ | 920/1501 [00:27<00:20, 28.81 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:27<00:03, 3.40s/ url]
Dl Size...: 61%|██████▏ | 920/1501 [00:27<00:20, 28.81 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:27<00:03, 3.40s/ url]
Dl Size...: 61%|██████▏ | 921/1501 [00:27<00:20, 28.81 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:27<00:03, 3.40s/ url]
Dl Size...: 61%|██████▏ | 922/1501 [00:27<00:20, 28.81 MiB/s]
Dl Size...: 61%|██████▏ | 923/1501 [00:27<00:20, 28.57 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:27<00:03, 3.40s/ url]
Dl Size...: 61%|██████▏ | 923/1501 [00:27<00:20, 28.57 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:27<00:03, 3.40s/ url]
Dl Size...: 62%|██████▏ | 924/1501 [00:27<00:20, 28.57 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:27<00:03, 3.40s/ url]
Dl Size...: 62%|██████▏ | 925/1501 [00:27<00:20, 28.57 MiB/s]
Dl Size...: 62%|██████▏ | 926/1501 [00:27<00:20, 28.34 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:27<00:03, 3.40s/ url]
Dl Size...: 62%|██████▏ | 926/1501 [00:27<00:20, 28.34 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:27<00:03, 3.40s/ url]
Dl Size...: 62%|██████▏ | 927/1501 [00:27<00:20, 28.34 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:27<00:03, 3.40s/ url]
Dl Size...: 62%|██████▏ | 928/1501 [00:27<00:20, 28.34 MiB/s]
Dl Size...: 62%|██████▏ | 929/1501 [00:27<00:20, 28.22 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:27<00:03, 3.40s/ url]
Dl Size...: 62%|██████▏ | 929/1501 [00:27<00:20, 28.22 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:27<00:03, 3.40s/ url]
Dl Size...: 62%|██████▏ | 930/1501 [00:27<00:20, 28.22 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:27<00:03, 3.40s/ url]
Dl Size...: 62%|██████▏ | 931/1501 [00:27<00:20, 28.22 MiB/s]
Dl Size...: 62%|██████▏ | 932/1501 [00:27<00:20, 28.34 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:27<00:03, 3.40s/ url]
Dl Size...: 62%|██████▏ | 932/1501 [00:27<00:20, 28.34 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:27<00:03, 3.40s/ url]
Dl Size...: 62%|██████▏ | 933/1501 [00:27<00:20, 28.34 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:27<00:03, 3.40s/ url]
Dl Size...: 62%|██████▏ | 934/1501 [00:27<00:20, 28.34 MiB/s]
Dl Size...: 62%|██████▏ | 935/1501 [00:27<00:20, 28.25 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:27<00:03, 3.40s/ url]
Dl Size...: 62%|██████▏ | 935/1501 [00:27<00:20, 28.25 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:27<00:03, 3.40s/ url]
Dl Size...: 62%|██████▏ | 936/1501 [00:27<00:20, 28.25 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:27<00:03, 3.40s/ url]
Dl Size...: 62%|██████▏ | 937/1501 [00:27<00:19, 28.25 MiB/s]
Dl Size...: 62%|██████▏ | 938/1501 [00:27<00:20, 28.11 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:27<00:03, 3.40s/ url]
Dl Size...: 62%|██████▏ | 938/1501 [00:27<00:20, 28.11 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:27<00:03, 3.40s/ url]
Dl Size...: 63%|██████▎ | 939/1501 [00:27<00:19, 28.11 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:27<00:03, 3.40s/ url]
Dl Size...: 63%|██████▎ | 940/1501 [00:27<00:19, 28.11 MiB/s]
Dl Size...: 63%|██████▎ | 941/1501 [00:27<00:20, 27.90 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:27<00:03, 3.40s/ url]
Dl Size...: 63%|██████▎ | 941/1501 [00:27<00:20, 27.90 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:27<00:03, 3.40s/ url]
Dl Size...: 63%|██████▎ | 942/1501 [00:27<00:20, 27.90 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:27<00:03, 3.40s/ url]
Dl Size...: 63%|██████▎ | 943/1501 [00:27<00:19, 27.90 MiB/s]
Dl Size...: 63%|██████▎ | 944/1501 [00:27<00:19, 28.05 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:27<00:03, 3.40s/ url]
Dl Size...: 63%|██████▎ | 944/1501 [00:27<00:19, 28.05 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:27<00:03, 3.40s/ url]
Dl Size...: 63%|██████▎ | 945/1501 [00:27<00:19, 28.05 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:27<00:03, 3.40s/ url]
Dl Size...: 63%|██████▎ | 946/1501 [00:27<00:19, 28.05 MiB/s]
Dl Size...: 63%|██████▎ | 947/1501 [00:28<00:19, 28.28 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:28<00:03, 3.40s/ url]
Dl Size...: 63%|██████▎ | 947/1501 [00:28<00:19, 28.28 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:28<00:03, 3.40s/ url]
Dl Size...: 63%|██████▎ | 948/1501 [00:28<00:19, 28.28 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:28<00:03, 3.40s/ url]
Dl Size...: 63%|██████▎ | 949/1501 [00:28<00:19, 28.28 MiB/s]
Dl Size...: 63%|██████▎ | 950/1501 [00:28<00:19, 27.80 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:28<00:03, 3.40s/ url]
Dl Size...: 63%|██████▎ | 950/1501 [00:28<00:19, 27.80 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:28<00:03, 3.40s/ url]
Dl Size...: 63%|██████▎ | 951/1501 [00:28<00:19, 27.80 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:28<00:03, 3.40s/ url]
Dl Size...: 63%|██████▎ | 952/1501 [00:28<00:19, 27.80 MiB/s]
Dl Size...: 63%|██████▎ | 953/1501 [00:28<00:19, 27.82 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:28<00:03, 3.40s/ url]
Dl Size...: 63%|██████▎ | 953/1501 [00:28<00:19, 27.82 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:28<00:03, 3.40s/ url]
Dl Size...: 64%|██████▎ | 954/1501 [00:28<00:19, 27.82 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:28<00:03, 3.40s/ url]
Dl Size...: 64%|██████▎ | 955/1501 [00:28<00:19, 27.82 MiB/s]
Dl Size...: 64%|██████▎ | 956/1501 [00:28<00:19, 27.68 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:28<00:03, 3.40s/ url]
Dl Size...: 64%|██████▎ | 956/1501 [00:28<00:19, 27.68 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:28<00:03, 3.40s/ url]
Dl Size...: 64%|██████▍ | 957/1501 [00:28<00:19, 27.68 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:28<00:03, 3.40s/ url]
Dl Size...: 64%|██████▍ | 958/1501 [00:28<00:19, 27.68 MiB/s]
Dl Size...: 64%|██████▍ | 959/1501 [00:28<00:19, 27.62 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:28<00:03, 3.40s/ url]
Dl Size...: 64%|██████▍ | 959/1501 [00:28<00:19, 27.62 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:28<00:03, 3.40s/ url]
Dl Size...: 64%|██████▍ | 960/1501 [00:28<00:19, 27.62 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:28<00:03, 3.40s/ url]
Dl Size...: 64%|██████▍ | 961/1501 [00:28<00:19, 27.62 MiB/s]
Dl Size...: 64%|██████▍ | 962/1501 [00:28<00:19, 27.46 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:28<00:03, 3.40s/ url]
Dl Size...: 64%|██████▍ | 962/1501 [00:28<00:19, 27.46 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:28<00:03, 3.40s/ url]
Dl Size...: 64%|██████▍ | 963/1501 [00:28<00:19, 27.46 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:28<00:03, 3.40s/ url]
Dl Size...: 64%|██████▍ | 964/1501 [00:28<00:19, 27.46 MiB/s]
Dl Size...: 64%|██████▍ | 965/1501 [00:28<00:19, 27.14 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:28<00:03, 3.40s/ url]
Dl Size...: 64%|██████▍ | 965/1501 [00:28<00:19, 27.14 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:28<00:03, 3.40s/ url]
Dl Size...: 64%|██████▍ | 966/1501 [00:28<00:19, 27.14 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:28<00:03, 3.40s/ url]
Dl Size...: 64%|██████▍ | 967/1501 [00:28<00:19, 27.14 MiB/s]
Dl Size...: 64%|██████▍ | 968/1501 [00:28<00:19, 27.00 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:28<00:03, 3.40s/ url]
Dl Size...: 64%|██████▍ | 968/1501 [00:28<00:19, 27.00 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:28<00:03, 3.40s/ url]
Dl Size...: 65%|██████▍ | 969/1501 [00:28<00:19, 27.00 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:28<00:03, 3.40s/ url]
Dl Size...: 65%|██████▍ | 970/1501 [00:28<00:19, 27.00 MiB/s]
Dl Size...: 65%|██████▍ | 971/1501 [00:28<00:19, 27.14 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:28<00:03, 3.40s/ url]
Dl Size...: 65%|██████▍ | 971/1501 [00:28<00:19, 27.14 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:28<00:03, 3.40s/ url]
Dl Size...: 65%|██████▍ | 972/1501 [00:28<00:19, 27.14 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:28<00:03, 3.40s/ url]
Dl Size...: 65%|██████▍ | 973/1501 [00:28<00:19, 27.14 MiB/s]
Dl Size...: 65%|██████▍ | 974/1501 [00:29<00:19, 27.51 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:29<00:03, 3.40s/ url]
Dl Size...: 65%|██████▍ | 974/1501 [00:29<00:19, 27.51 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:29<00:03, 3.40s/ url]
Dl Size...: 65%|██████▍ | 975/1501 [00:29<00:19, 27.51 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:29<00:03, 3.40s/ url]
Dl Size...: 65%|██████▌ | 976/1501 [00:29<00:19, 27.51 MiB/s]
Dl Size...: 65%|██████▌ | 977/1501 [00:29<00:18, 28.17 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:29<00:03, 3.40s/ url]
Dl Size...: 65%|██████▌ | 977/1501 [00:29<00:18, 28.17 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:29<00:03, 3.40s/ url]
Dl Size...: 65%|██████▌ | 978/1501 [00:29<00:18, 28.17 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:29<00:03, 3.40s/ url]
Dl Size...: 65%|██████▌ | 979/1501 [00:29<00:18, 28.17 MiB/s]
Dl Size...: 65%|██████▌ | 980/1501 [00:29<00:18, 28.33 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:29<00:03, 3.40s/ url]
Dl Size...: 65%|██████▌ | 980/1501 [00:29<00:18, 28.33 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:29<00:03, 3.40s/ url]
Dl Size...: 65%|██████▌ | 981/1501 [00:29<00:18, 28.33 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:29<00:03, 3.40s/ url]
Dl Size...: 65%|██████▌ | 982/1501 [00:29<00:18, 28.33 MiB/s]
Dl Size...: 65%|██████▌ | 983/1501 [00:29<00:18, 28.46 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:29<00:03, 3.40s/ url]
Dl Size...: 65%|██████▌ | 983/1501 [00:29<00:18, 28.46 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:29<00:03, 3.40s/ url]
Dl Size...: 66%|██████▌ | 984/1501 [00:29<00:18, 28.46 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:29<00:03, 3.40s/ url]
Dl Size...: 66%|██████▌ | 985/1501 [00:29<00:18, 28.46 MiB/s]
Dl Size...: 66%|██████▌ | 986/1501 [00:29<00:17, 28.78 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:29<00:03, 3.40s/ url]
Dl Size...: 66%|██████▌ | 986/1501 [00:29<00:17, 28.78 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:29<00:03, 3.40s/ url]
Dl Size...: 66%|██████▌ | 987/1501 [00:29<00:17, 28.78 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:29<00:03, 3.40s/ url]
Dl Size...: 66%|██████▌ | 988/1501 [00:29<00:17, 28.78 MiB/s]
Dl Size...: 66%|██████▌ | 989/1501 [00:29<00:17, 28.77 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:29<00:03, 3.40s/ url]
Dl Size...: 66%|██████▌ | 989/1501 [00:29<00:17, 28.77 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:29<00:03, 3.40s/ url]
Dl Size...: 66%|██████▌ | 990/1501 [00:29<00:17, 28.77 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:29<00:03, 3.40s/ url]
Dl Size...: 66%|██████▌ | 991/1501 [00:29<00:17, 28.77 MiB/s]
Dl Size...: 66%|██████▌ | 992/1501 [00:29<00:17, 28.78 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:29<00:03, 3.40s/ url]
Dl Size...: 66%|██████▌ | 992/1501 [00:29<00:17, 28.78 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:29<00:03, 3.40s/ url]
Dl Size...: 66%|██████▌ | 993/1501 [00:29<00:17, 28.78 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:29<00:03, 3.40s/ url]
Dl Size...: 66%|██████▌ | 994/1501 [00:29<00:17, 28.78 MiB/s]
Dl Size...: 66%|██████▋ | 995/1501 [00:29<00:17, 28.70 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:29<00:03, 3.40s/ url]
Dl Size...: 66%|██████▋ | 995/1501 [00:29<00:17, 28.70 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:29<00:03, 3.40s/ url]
Dl Size...: 66%|██████▋ | 996/1501 [00:29<00:17, 28.70 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:29<00:03, 3.40s/ url]
Dl Size...: 66%|██████▋ | 997/1501 [00:29<00:17, 28.70 MiB/s]
Dl Size...: 66%|██████▋ | 998/1501 [00:29<00:17, 28.74 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:29<00:03, 3.40s/ url]
Dl Size...: 66%|██████▋ | 998/1501 [00:29<00:17, 28.74 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:29<00:03, 3.40s/ url]
Dl Size...: 67%|██████▋ | 999/1501 [00:29<00:17, 28.74 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:29<00:03, 3.40s/ url]
Dl Size...: 67%|██████▋ | 1000/1501 [00:29<00:17, 28.74 MiB/s]
Dl Size...: 67%|██████▋ | 1001/1501 [00:29<00:17, 28.65 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:29<00:03, 3.40s/ url]
Dl Size...: 67%|██████▋ | 1001/1501 [00:29<00:17, 28.65 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:29<00:03, 3.40s/ url]
Dl Size...: 67%|██████▋ | 1002/1501 [00:29<00:17, 28.65 MiB/s]
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Dl Size...: 67%|██████▋ | 1003/1501 [00:30<00:17, 28.65 MiB/s]
Dl Size...: 67%|██████▋ | 1004/1501 [00:30<00:17, 28.25 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:30<00:03, 3.40s/ url]
Dl Size...: 67%|██████▋ | 1004/1501 [00:30<00:17, 28.25 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:30<00:03, 3.40s/ url]
Dl Size...: 67%|██████▋ | 1005/1501 [00:30<00:17, 28.25 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:30<00:03, 3.40s/ url]
Dl Size...: 67%|██████▋ | 1006/1501 [00:30<00:17, 28.25 MiB/s]
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Dl Completed...: 67%|██████▋ | 2/3 [00:30<00:03, 3.40s/ url]
Dl Size...: 67%|██████▋ | 1007/1501 [00:30<00:17, 28.53 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:30<00:03, 3.40s/ url]
Dl Size...: 67%|██████▋ | 1008/1501 [00:30<00:17, 28.53 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:30<00:03, 3.40s/ url]
Dl Size...: 67%|██████▋ | 1009/1501 [00:30<00:17, 28.53 MiB/s]
Dl Size...: 67%|██████▋ | 1010/1501 [00:30<00:17, 28.58 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:30<00:03, 3.40s/ url]
Dl Size...: 67%|██████▋ | 1010/1501 [00:30<00:17, 28.58 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:30<00:03, 3.40s/ url]
Dl Size...: 67%|██████▋ | 1011/1501 [00:30<00:17, 28.58 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:30<00:03, 3.40s/ url]
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Dl Size...: 67%|██████▋ | 1013/1501 [00:30<00:17, 28.60 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:30<00:03, 3.40s/ url]
Dl Size...: 67%|██████▋ | 1013/1501 [00:30<00:17, 28.60 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:30<00:03, 3.40s/ url]
Dl Size...: 68%|██████▊ | 1014/1501 [00:30<00:17, 28.60 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:30<00:03, 3.40s/ url]
Dl Size...: 68%|██████▊ | 1015/1501 [00:30<00:16, 28.60 MiB/s]
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Dl Completed...: 67%|██████▋ | 2/3 [00:30<00:03, 3.40s/ url]
Dl Size...: 68%|██████▊ | 1016/1501 [00:30<00:16, 28.65 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:30<00:03, 3.40s/ url]
Dl Size...: 68%|██████▊ | 1017/1501 [00:30<00:16, 28.65 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:30<00:03, 3.40s/ url]
Dl Size...: 68%|██████▊ | 1018/1501 [00:30<00:16, 28.65 MiB/s]
Dl Size...: 68%|██████▊ | 1019/1501 [00:30<00:16, 28.94 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:30<00:03, 3.40s/ url]
Dl Size...: 68%|██████▊ | 1019/1501 [00:30<00:16, 28.94 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:30<00:03, 3.40s/ url]
Dl Size...: 68%|██████▊ | 1020/1501 [00:30<00:16, 28.94 MiB/s]
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Dl Size...: 68%|██████▊ | 1021/1501 [00:30<00:16, 28.94 MiB/s]
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Dl Completed...: 67%|██████▋ | 2/3 [00:30<00:03, 3.40s/ url]
Dl Size...: 68%|██████▊ | 1022/1501 [00:30<00:16, 29.04 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:30<00:03, 3.40s/ url]
Dl Size...: 68%|██████▊ | 1023/1501 [00:30<00:16, 29.04 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:30<00:03, 3.40s/ url]
Dl Size...: 68%|██████▊ | 1024/1501 [00:30<00:16, 29.04 MiB/s]
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Dl Completed...: 67%|██████▋ | 2/3 [00:30<00:03, 3.40s/ url]
Dl Size...: 68%|██████▊ | 1025/1501 [00:30<00:16, 28.97 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:30<00:03, 3.40s/ url]
Dl Size...: 68%|██████▊ | 1026/1501 [00:30<00:16, 28.97 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:30<00:03, 3.40s/ url]
Dl Size...: 68%|██████▊ | 1027/1501 [00:30<00:16, 28.97 MiB/s]
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Dl Completed...: 67%|██████▋ | 2/3 [00:30<00:03, 3.40s/ url]
Dl Size...: 68%|██████▊ | 1028/1501 [00:30<00:16, 29.25 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:30<00:03, 3.40s/ url]
Dl Size...: 69%|██████▊ | 1029/1501 [00:30<00:16, 29.25 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:30<00:03, 3.40s/ url]
Dl Size...: 69%|██████▊ | 1030/1501 [00:30<00:16, 29.25 MiB/s]
Dl Size...: 69%|██████▊ | 1031/1501 [00:30<00:16, 29.30 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:30<00:03, 3.40s/ url]
Dl Size...: 69%|██████▊ | 1031/1501 [00:30<00:16, 29.30 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:31<00:03, 3.40s/ url]
Dl Size...: 69%|██████▉ | 1032/1501 [00:31<00:16, 29.30 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:31<00:03, 3.40s/ url]
Dl Size...: 69%|██████▉ | 1033/1501 [00:31<00:15, 29.30 MiB/s]
Dl Size...: 69%|██████▉ | 1034/1501 [00:31<00:15, 29.48 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:31<00:03, 3.40s/ url]
Dl Size...: 69%|██████▉ | 1034/1501 [00:31<00:15, 29.48 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:31<00:03, 3.40s/ url]
Dl Size...: 69%|██████▉ | 1035/1501 [00:31<00:15, 29.48 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:31<00:03, 3.40s/ url]
Dl Size...: 69%|██████▉ | 1036/1501 [00:31<00:15, 29.48 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:31<00:03, 3.40s/ url]
Dl Size...: 69%|██████▉ | 1037/1501 [00:31<00:15, 29.48 MiB/s]
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Dl Completed...: 67%|██████▋ | 2/3 [00:31<00:03, 3.40s/ url]
Dl Size...: 69%|██████▉ | 1038/1501 [00:31<00:15, 29.85 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:31<00:03, 3.40s/ url]
Dl Size...: 69%|██████▉ | 1039/1501 [00:31<00:15, 29.85 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:31<00:03, 3.40s/ url]
Dl Size...: 69%|██████▉ | 1040/1501 [00:31<00:15, 29.85 MiB/s]
Dl Size...: 69%|██████▉ | 1041/1501 [00:31<00:15, 29.14 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:31<00:03, 3.40s/ url]
Dl Size...: 69%|██████▉ | 1041/1501 [00:31<00:15, 29.14 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:31<00:03, 3.40s/ url]
Dl Size...: 69%|██████▉ | 1042/1501 [00:31<00:15, 29.14 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:31<00:03, 3.40s/ url]
Dl Size...: 69%|██████▉ | 1043/1501 [00:31<00:15, 29.14 MiB/s]
Dl Size...: 70%|██████▉ | 1044/1501 [00:31<00:15, 28.62 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:31<00:03, 3.40s/ url]
Dl Size...: 70%|██████▉ | 1044/1501 [00:31<00:15, 28.62 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:31<00:03, 3.40s/ url]
Dl Size...: 70%|██████▉ | 1045/1501 [00:31<00:15, 28.62 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:31<00:03, 3.40s/ url]
Dl Size...: 70%|██████▉ | 1046/1501 [00:31<00:15, 28.62 MiB/s]
Dl Size...: 70%|██████▉ | 1047/1501 [00:31<00:15, 28.61 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:31<00:03, 3.40s/ url]
Dl Size...: 70%|██████▉ | 1047/1501 [00:31<00:15, 28.61 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:31<00:03, 3.40s/ url]
Dl Size...: 70%|██████▉ | 1048/1501 [00:31<00:15, 28.61 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:31<00:03, 3.40s/ url]
Dl Size...: 70%|██████▉ | 1049/1501 [00:31<00:15, 28.61 MiB/s]
Dl Size...: 70%|██████▉ | 1050/1501 [00:31<00:15, 28.85 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:31<00:03, 3.40s/ url]
Dl Size...: 70%|██████▉ | 1050/1501 [00:31<00:15, 28.85 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:31<00:03, 3.40s/ url]
Dl Size...: 70%|███████ | 1051/1501 [00:31<00:15, 28.85 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:31<00:03, 3.40s/ url]
Dl Size...: 70%|███████ | 1052/1501 [00:31<00:15, 28.85 MiB/s]
Dl Size...: 70%|███████ | 1053/1501 [00:31<00:15, 28.78 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:31<00:03, 3.40s/ url]
Dl Size...: 70%|███████ | 1053/1501 [00:31<00:15, 28.78 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:31<00:03, 3.40s/ url]
Dl Size...: 70%|███████ | 1054/1501 [00:31<00:15, 28.78 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:31<00:03, 3.40s/ url]
Dl Size...: 70%|███████ | 1055/1501 [00:31<00:15, 28.78 MiB/s]
Dl Size...: 70%|███████ | 1056/1501 [00:31<00:15, 28.80 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:31<00:03, 3.40s/ url]
Dl Size...: 70%|███████ | 1056/1501 [00:31<00:15, 28.80 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:31<00:03, 3.40s/ url]
Dl Size...: 70%|███████ | 1057/1501 [00:31<00:15, 28.80 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:31<00:03, 3.40s/ url]
Dl Size...: 70%|███████ | 1058/1501 [00:31<00:15, 28.80 MiB/s]
Dl Size...: 71%|███████ | 1059/1501 [00:31<00:15, 28.71 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:31<00:03, 3.40s/ url]
Dl Size...: 71%|███████ | 1059/1501 [00:31<00:15, 28.71 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:31<00:03, 3.40s/ url]
Dl Size...: 71%|███████ | 1060/1501 [00:31<00:15, 28.71 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:32<00:03, 3.40s/ url]
Dl Size...: 71%|███████ | 1061/1501 [00:32<00:15, 28.71 MiB/s]
Dl Size...: 71%|███████ | 1062/1501 [00:32<00:15, 28.79 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:32<00:03, 3.40s/ url]
Dl Size...: 71%|███████ | 1062/1501 [00:32<00:15, 28.79 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:32<00:03, 3.40s/ url]
Dl Size...: 71%|███████ | 1063/1501 [00:32<00:15, 28.79 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:32<00:03, 3.40s/ url]
Dl Size...: 71%|███████ | 1064/1501 [00:32<00:15, 28.79 MiB/s]
Dl Size...: 71%|███████ | 1065/1501 [00:32<00:15, 28.95 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:32<00:03, 3.40s/ url]
Dl Size...: 71%|███████ | 1065/1501 [00:32<00:15, 28.95 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:32<00:03, 3.40s/ url]
Dl Size...: 71%|███████ | 1066/1501 [00:32<00:15, 28.95 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:32<00:03, 3.40s/ url]
Dl Size...: 71%|███████ | 1067/1501 [00:32<00:14, 28.95 MiB/s]
Dl Size...: 71%|███████ | 1068/1501 [00:32<00:14, 29.03 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:32<00:03, 3.40s/ url]
Dl Size...: 71%|███████ | 1068/1501 [00:32<00:14, 29.03 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:32<00:03, 3.40s/ url]
Dl Size...: 71%|███████ | 1069/1501 [00:32<00:14, 29.03 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:32<00:03, 3.40s/ url]
Dl Size...: 71%|███████▏ | 1070/1501 [00:32<00:14, 29.03 MiB/s]
Dl Size...: 71%|███████▏ | 1071/1501 [00:32<00:14, 29.08 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:32<00:03, 3.40s/ url]
Dl Size...: 71%|███████▏ | 1071/1501 [00:32<00:14, 29.08 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:32<00:03, 3.40s/ url]
Dl Size...: 71%|███████▏ | 1072/1501 [00:32<00:14, 29.08 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:32<00:03, 3.40s/ url]
Dl Size...: 71%|███████▏ | 1073/1501 [00:32<00:14, 29.08 MiB/s]
Dl Size...: 72%|███████▏ | 1074/1501 [00:32<00:14, 28.75 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:32<00:03, 3.40s/ url]
Dl Size...: 72%|███████▏ | 1074/1501 [00:32<00:14, 28.75 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:32<00:03, 3.40s/ url]
Dl Size...: 72%|███████▏ | 1075/1501 [00:32<00:14, 28.75 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:32<00:03, 3.40s/ url]
Dl Size...: 72%|███████▏ | 1076/1501 [00:32<00:14, 28.75 MiB/s]
Dl Size...: 72%|███████▏ | 1077/1501 [00:32<00:14, 28.81 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:32<00:03, 3.40s/ url]
Dl Size...: 72%|███████▏ | 1077/1501 [00:32<00:14, 28.81 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:32<00:03, 3.40s/ url]
Dl Size...: 72%|███████▏ | 1078/1501 [00:32<00:14, 28.81 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:32<00:03, 3.40s/ url]
Dl Size...: 72%|███████▏ | 1079/1501 [00:32<00:14, 28.81 MiB/s]
Dl Size...: 72%|███████▏ | 1080/1501 [00:32<00:14, 28.89 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:32<00:03, 3.40s/ url]
Dl Size...: 72%|███████▏ | 1080/1501 [00:32<00:14, 28.89 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:32<00:03, 3.40s/ url]
Dl Size...: 72%|███████▏ | 1081/1501 [00:32<00:14, 28.89 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:32<00:03, 3.40s/ url]
Dl Size...: 72%|███████▏ | 1082/1501 [00:32<00:14, 28.89 MiB/s]
Dl Size...: 72%|███████▏ | 1083/1501 [00:32<00:14, 28.78 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:32<00:03, 3.40s/ url]
Dl Size...: 72%|███████▏ | 1083/1501 [00:32<00:14, 28.78 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:32<00:03, 3.40s/ url]
Dl Size...: 72%|███████▏ | 1084/1501 [00:32<00:14, 28.78 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:32<00:03, 3.40s/ url]
Dl Size...: 72%|███████▏ | 1085/1501 [00:32<00:14, 28.78 MiB/s]
Dl Size...: 72%|███████▏ | 1086/1501 [00:32<00:14, 28.83 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:32<00:03, 3.40s/ url]
Dl Size...: 72%|███████▏ | 1086/1501 [00:32<00:14, 28.83 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:32<00:03, 3.40s/ url]
Dl Size...: 72%|███████▏ | 1087/1501 [00:32<00:14, 28.83 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:32<00:03, 3.40s/ url]
Dl Size...: 72%|███████▏ | 1088/1501 [00:32<00:14, 28.83 MiB/s]
Dl Size...: 73%|███████▎ | 1089/1501 [00:32<00:14, 28.76 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:33<00:03, 3.40s/ url]
Dl Size...: 73%|███████▎ | 1089/1501 [00:33<00:14, 28.76 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:33<00:03, 3.40s/ url]
Dl Size...: 73%|███████▎ | 1090/1501 [00:33<00:14, 28.76 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:33<00:03, 3.40s/ url]
Dl Size...: 73%|███████▎ | 1091/1501 [00:33<00:14, 28.76 MiB/s]
Dl Size...: 73%|███████▎ | 1092/1501 [00:33<00:14, 29.07 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:33<00:03, 3.40s/ url]
Dl Size...: 73%|███████▎ | 1092/1501 [00:33<00:14, 29.07 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:33<00:03, 3.40s/ url]
Dl Size...: 73%|███████▎ | 1093/1501 [00:33<00:14, 29.07 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:33<00:03, 3.40s/ url]
Dl Size...: 73%|███████▎ | 1094/1501 [00:33<00:14, 29.07 MiB/s]
Dl Size...: 73%|███████▎ | 1095/1501 [00:33<00:13, 29.23 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:33<00:03, 3.40s/ url]
Dl Size...: 73%|███████▎ | 1095/1501 [00:33<00:13, 29.23 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:33<00:03, 3.40s/ url]
Dl Size...: 73%|███████▎ | 1096/1501 [00:33<00:13, 29.23 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:33<00:03, 3.40s/ url]
Dl Size...: 73%|███████▎ | 1097/1501 [00:33<00:13, 29.23 MiB/s]
Dl Size...: 73%|███████▎ | 1098/1501 [00:33<00:13, 29.10 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:33<00:03, 3.40s/ url]
Dl Size...: 73%|███████▎ | 1098/1501 [00:33<00:13, 29.10 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:33<00:03, 3.40s/ url]
Dl Size...: 73%|███████▎ | 1099/1501 [00:33<00:13, 29.10 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:33<00:03, 3.40s/ url]
Dl Size...: 73%|███████▎ | 1100/1501 [00:33<00:13, 29.10 MiB/s]
Dl Size...: 73%|███████▎ | 1101/1501 [00:33<00:13, 29.04 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:33<00:03, 3.40s/ url]
Dl Size...: 73%|███████▎ | 1101/1501 [00:33<00:13, 29.04 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:33<00:03, 3.40s/ url]
Dl Size...: 73%|███████▎ | 1102/1501 [00:33<00:13, 29.04 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:33<00:03, 3.40s/ url]
Dl Size...: 73%|███████▎ | 1103/1501 [00:33<00:13, 29.04 MiB/s]
Dl Size...: 74%|███████▎ | 1104/1501 [00:33<00:13, 29.08 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:33<00:03, 3.40s/ url]
Dl Size...: 74%|███████▎ | 1104/1501 [00:33<00:13, 29.08 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:33<00:03, 3.40s/ url]
Dl Size...: 74%|███████▎ | 1105/1501 [00:33<00:13, 29.08 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:33<00:03, 3.40s/ url]
Dl Size...: 74%|███████▎ | 1106/1501 [00:33<00:13, 29.08 MiB/s]
Dl Size...: 74%|███████▍ | 1107/1501 [00:33<00:13, 29.01 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:33<00:03, 3.40s/ url]
Dl Size...: 74%|███████▍ | 1107/1501 [00:33<00:13, 29.01 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:33<00:03, 3.40s/ url]
Dl Size...: 74%|███████▍ | 1108/1501 [00:33<00:13, 29.01 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:33<00:03, 3.40s/ url]
Dl Size...: 74%|███████▍ | 1109/1501 [00:33<00:13, 29.01 MiB/s]
Dl Size...: 74%|███████▍ | 1110/1501 [00:33<00:13, 29.16 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:33<00:03, 3.40s/ url]
Dl Size...: 74%|███████▍ | 1110/1501 [00:33<00:13, 29.16 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:33<00:03, 3.40s/ url]
Dl Size...: 74%|███████▍ | 1111/1501 [00:33<00:13, 29.16 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:33<00:03, 3.40s/ url]
Dl Size...: 74%|███████▍ | 1112/1501 [00:33<00:13, 29.16 MiB/s]
Dl Size...: 74%|███████▍ | 1113/1501 [00:33<00:13, 28.74 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:33<00:03, 3.40s/ url]
Dl Size...: 74%|███████▍ | 1113/1501 [00:33<00:13, 28.74 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:33<00:03, 3.40s/ url]
Dl Size...: 74%|███████▍ | 1114/1501 [00:33<00:13, 28.74 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:33<00:03, 3.40s/ url]
Dl Size...: 74%|███████▍ | 1115/1501 [00:33<00:13, 28.74 MiB/s]
Dl Size...: 74%|███████▍ | 1116/1501 [00:33<00:13, 28.72 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:33<00:03, 3.40s/ url]
Dl Size...: 74%|███████▍ | 1116/1501 [00:33<00:13, 28.72 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:33<00:03, 3.40s/ url]
Dl Size...: 74%|███████▍ | 1117/1501 [00:33<00:13, 28.72 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:33<00:03, 3.40s/ url]
Dl Size...: 74%|███████▍ | 1118/1501 [00:33<00:13, 28.72 MiB/s]
Dl Size...: 75%|███████▍ | 1119/1501 [00:34<00:13, 28.68 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:34<00:03, 3.40s/ url]
Dl Size...: 75%|███████▍ | 1119/1501 [00:34<00:13, 28.68 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:34<00:03, 3.40s/ url]
Dl Size...: 75%|███████▍ | 1120/1501 [00:34<00:13, 28.68 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:34<00:03, 3.40s/ url]
Dl Size...: 75%|███████▍ | 1121/1501 [00:34<00:13, 28.68 MiB/s]
Dl Size...: 75%|███████▍ | 1122/1501 [00:34<00:13, 28.74 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:34<00:03, 3.40s/ url]
Dl Size...: 75%|███████▍ | 1122/1501 [00:34<00:13, 28.74 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:34<00:03, 3.40s/ url]
Dl Size...: 75%|███████▍ | 1123/1501 [00:34<00:13, 28.74 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:34<00:03, 3.40s/ url]
Dl Size...: 75%|███████▍ | 1124/1501 [00:34<00:13, 28.74 MiB/s]
Dl Size...: 75%|███████▍ | 1125/1501 [00:34<00:13, 28.88 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:34<00:03, 3.40s/ url]
Dl Size...: 75%|███████▍ | 1125/1501 [00:34<00:13, 28.88 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:34<00:03, 3.40s/ url]
Dl Size...: 75%|███████▌ | 1126/1501 [00:34<00:12, 28.88 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:34<00:03, 3.40s/ url]
Dl Size...: 75%|███████▌ | 1127/1501 [00:34<00:12, 28.88 MiB/s]
Dl Size...: 75%|███████▌ | 1128/1501 [00:34<00:13, 28.36 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:34<00:03, 3.40s/ url]
Dl Size...: 75%|███████▌ | 1128/1501 [00:34<00:13, 28.36 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:34<00:03, 3.40s/ url]
Dl Size...: 75%|███████▌ | 1129/1501 [00:34<00:13, 28.36 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:34<00:03, 3.40s/ url]
Dl Size...: 75%|███████▌ | 1130/1501 [00:34<00:13, 28.36 MiB/s]
Dl Size...: 75%|███████▌ | 1131/1501 [00:34<00:13, 28.02 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:34<00:03, 3.40s/ url]
Dl Size...: 75%|███████▌ | 1131/1501 [00:34<00:13, 28.02 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:34<00:03, 3.40s/ url]
Dl Size...: 75%|███████▌ | 1132/1501 [00:34<00:13, 28.02 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:34<00:03, 3.40s/ url]
Dl Size...: 75%|███████▌ | 1133/1501 [00:34<00:13, 28.02 MiB/s]
Dl Size...: 76%|███████▌ | 1134/1501 [00:34<00:12, 28.36 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:34<00:03, 3.40s/ url]
Dl Size...: 76%|███████▌ | 1134/1501 [00:34<00:12, 28.36 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:34<00:03, 3.40s/ url]
Dl Size...: 76%|███████▌ | 1135/1501 [00:34<00:12, 28.36 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:34<00:03, 3.40s/ url]
Dl Size...: 76%|███████▌ | 1136/1501 [00:34<00:12, 28.36 MiB/s]
Dl Size...: 76%|███████▌ | 1137/1501 [00:34<00:12, 28.07 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:34<00:03, 3.40s/ url]
Dl Size...: 76%|███████▌ | 1137/1501 [00:34<00:12, 28.07 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:34<00:03, 3.40s/ url]
Dl Size...: 76%|███████▌ | 1138/1501 [00:34<00:12, 28.07 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:34<00:03, 3.40s/ url]
Dl Size...: 76%|███████▌ | 1139/1501 [00:34<00:12, 28.07 MiB/s]
Dl Size...: 76%|███████▌ | 1140/1501 [00:34<00:12, 28.42 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:34<00:03, 3.40s/ url]
Dl Size...: 76%|███████▌ | 1140/1501 [00:34<00:12, 28.42 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:34<00:03, 3.40s/ url]
Dl Size...: 76%|███████▌ | 1141/1501 [00:34<00:12, 28.42 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:34<00:03, 3.40s/ url]
Dl Size...: 76%|███████▌ | 1142/1501 [00:34<00:12, 28.42 MiB/s]
Dl Size...: 76%|███████▌ | 1143/1501 [00:34<00:12, 28.22 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:34<00:03, 3.40s/ url]
Dl Size...: 76%|███████▌ | 1143/1501 [00:34<00:12, 28.22 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:34<00:03, 3.40s/ url]
Dl Size...: 76%|███████▌ | 1144/1501 [00:34<00:12, 28.22 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:34<00:03, 3.40s/ url]
Dl Size...: 76%|███████▋ | 1145/1501 [00:34<00:12, 28.22 MiB/s]
Dl Size...: 76%|███████▋ | 1146/1501 [00:34<00:12, 28.10 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:34<00:03, 3.40s/ url]
Dl Size...: 76%|███████▋ | 1146/1501 [00:34<00:12, 28.10 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:35<00:03, 3.40s/ url]
Dl Size...: 76%|███████▋ | 1147/1501 [00:35<00:12, 28.10 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:35<00:03, 3.40s/ url]
Dl Size...: 76%|███████▋ | 1148/1501 [00:35<00:12, 28.10 MiB/s]
Dl Size...: 77%|███████▋ | 1149/1501 [00:35<00:12, 28.24 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:35<00:03, 3.40s/ url]
Dl Size...: 77%|███████▋ | 1149/1501 [00:35<00:12, 28.24 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:35<00:03, 3.40s/ url]
Dl Size...: 77%|███████▋ | 1150/1501 [00:35<00:12, 28.24 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:35<00:03, 3.40s/ url]
Dl Size...: 77%|███████▋ | 1151/1501 [00:35<00:12, 28.24 MiB/s]
Dl Size...: 77%|███████▋ | 1152/1501 [00:35<00:12, 28.21 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:35<00:03, 3.40s/ url]
Dl Size...: 77%|███████▋ | 1152/1501 [00:35<00:12, 28.21 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:35<00:03, 3.40s/ url]
Dl Size...: 77%|███████▋ | 1153/1501 [00:35<00:12, 28.21 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:35<00:03, 3.40s/ url]
Dl Size...: 77%|███████▋ | 1154/1501 [00:35<00:12, 28.21 MiB/s]
Dl Size...: 77%|███████▋ | 1155/1501 [00:35<00:12, 27.76 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:35<00:03, 3.40s/ url]
Dl Size...: 77%|███████▋ | 1155/1501 [00:35<00:12, 27.76 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:35<00:03, 3.40s/ url]
Dl Size...: 77%|███████▋ | 1156/1501 [00:35<00:12, 27.76 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:35<00:03, 3.40s/ url]
Dl Size...: 77%|███████▋ | 1157/1501 [00:35<00:12, 27.76 MiB/s]
Dl Size...: 77%|███████▋ | 1158/1501 [00:35<00:12, 28.09 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:35<00:03, 3.40s/ url]
Dl Size...: 77%|███████▋ | 1158/1501 [00:35<00:12, 28.09 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:35<00:03, 3.40s/ url]
Dl Size...: 77%|███████▋ | 1159/1501 [00:35<00:12, 28.09 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:35<00:03, 3.40s/ url]
Dl Size...: 77%|███████▋ | 1160/1501 [00:35<00:12, 28.09 MiB/s]
Dl Size...: 77%|███████▋ | 1161/1501 [00:35<00:12, 27.60 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:35<00:03, 3.40s/ url]
Dl Size...: 77%|███████▋ | 1161/1501 [00:35<00:12, 27.60 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:35<00:03, 3.40s/ url]
Dl Size...: 77%|███████▋ | 1162/1501 [00:35<00:12, 27.60 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:35<00:03, 3.40s/ url]
Dl Size...: 77%|███████▋ | 1163/1501 [00:35<00:12, 27.60 MiB/s]
Dl Size...: 78%|███████▊ | 1164/1501 [00:35<00:12, 27.15 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:35<00:03, 3.40s/ url]
Dl Size...: 78%|███████▊ | 1164/1501 [00:35<00:12, 27.15 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:35<00:03, 3.40s/ url]
Dl Size...: 78%|███████▊ | 1165/1501 [00:35<00:12, 27.15 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:35<00:03, 3.40s/ url]
Dl Size...: 78%|███████▊ | 1166/1501 [00:35<00:12, 27.15 MiB/s]
Dl Size...: 78%|███████▊ | 1167/1501 [00:35<00:11, 27.87 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:35<00:03, 3.40s/ url]
Dl Size...: 78%|███████▊ | 1167/1501 [00:35<00:11, 27.87 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:35<00:03, 3.40s/ url]
Dl Size...: 78%|███████▊ | 1168/1501 [00:35<00:11, 27.87 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:35<00:03, 3.40s/ url]
Dl Size...: 78%|███████▊ | 1169/1501 [00:35<00:11, 27.87 MiB/s]
Dl Size...: 78%|███████▊ | 1170/1501 [00:35<00:12, 27.19 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:35<00:03, 3.40s/ url]
Dl Size...: 78%|███████▊ | 1170/1501 [00:35<00:12, 27.19 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:35<00:03, 3.40s/ url]
Dl Size...: 78%|███████▊ | 1171/1501 [00:35<00:12, 27.19 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:35<00:03, 3.40s/ url]
Dl Size...: 78%|███████▊ | 1172/1501 [00:35<00:12, 27.19 MiB/s]
Dl Size...: 78%|███████▊ | 1173/1501 [00:35<00:12, 26.54 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:35<00:03, 3.40s/ url]
Dl Size...: 78%|███████▊ | 1173/1501 [00:35<00:12, 26.54 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:36<00:03, 3.40s/ url]
Dl Size...: 78%|███████▊ | 1174/1501 [00:36<00:12, 26.54 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:36<00:03, 3.40s/ url]
Dl Size...: 78%|███████▊ | 1175/1501 [00:36<00:12, 26.54 MiB/s]
Dl Size...: 78%|███████▊ | 1176/1501 [00:36<00:11, 27.30 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:36<00:03, 3.40s/ url]
Dl Size...: 78%|███████▊ | 1176/1501 [00:36<00:11, 27.30 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:36<00:03, 3.40s/ url]
Dl Size...: 78%|███████▊ | 1177/1501 [00:36<00:11, 27.30 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:36<00:03, 3.40s/ url]
Dl Size...: 78%|███████▊ | 1178/1501 [00:36<00:11, 27.30 MiB/s]
Dl Size...: 79%|███████▊ | 1179/1501 [00:36<00:12, 26.56 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:36<00:03, 3.40s/ url]
Dl Size...: 79%|███████▊ | 1179/1501 [00:36<00:12, 26.56 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:36<00:03, 3.40s/ url]
Dl Size...: 79%|███████▊ | 1180/1501 [00:36<00:12, 26.56 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:36<00:03, 3.40s/ url]
Dl Size...: 79%|███████▊ | 1181/1501 [00:36<00:12, 26.56 MiB/s]
Dl Size...: 79%|███████▊ | 1182/1501 [00:36<00:12, 25.93 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:36<00:03, 3.40s/ url]
Dl Size...: 79%|███████▊ | 1182/1501 [00:36<00:12, 25.93 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:36<00:03, 3.40s/ url]
Dl Size...: 79%|███████▉ | 1183/1501 [00:36<00:12, 25.93 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:36<00:03, 3.40s/ url]
Dl Size...: 79%|███████▉ | 1184/1501 [00:36<00:12, 25.93 MiB/s]
Dl Size...: 79%|███████▉ | 1185/1501 [00:36<00:11, 27.01 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:36<00:03, 3.40s/ url]
Dl Size...: 79%|███████▉ | 1185/1501 [00:36<00:11, 27.01 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:36<00:03, 3.40s/ url]
Dl Size...: 79%|███████▉ | 1186/1501 [00:36<00:11, 27.01 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:36<00:03, 3.40s/ url]
Dl Size...: 79%|███████▉ | 1187/1501 [00:36<00:11, 27.01 MiB/s]
Dl Size...: 79%|███████▉ | 1188/1501 [00:36<00:11, 26.15 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:36<00:03, 3.40s/ url]
Dl Size...: 79%|███████▉ | 1188/1501 [00:36<00:11, 26.15 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:36<00:03, 3.40s/ url]
Dl Size...: 79%|███████▉ | 1189/1501 [00:36<00:11, 26.15 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:36<00:03, 3.40s/ url]
Dl Size...: 79%|███████▉ | 1190/1501 [00:36<00:11, 26.15 MiB/s]
Dl Size...: 79%|███████▉ | 1191/1501 [00:36<00:12, 25.34 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:36<00:03, 3.40s/ url]
Dl Size...: 79%|███████▉ | 1191/1501 [00:36<00:12, 25.34 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:36<00:03, 3.40s/ url]
Dl Size...: 79%|███████▉ | 1192/1501 [00:36<00:12, 25.34 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:36<00:03, 3.40s/ url]
Dl Size...: 79%|███████▉ | 1193/1501 [00:36<00:12, 25.34 MiB/s]
Dl Size...: 80%|███████▉ | 1194/1501 [00:36<00:11, 26.57 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:36<00:03, 3.40s/ url]
Dl Size...: 80%|███████▉ | 1194/1501 [00:36<00:11, 26.57 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:36<00:03, 3.40s/ url]
Dl Size...: 80%|███████▉ | 1195/1501 [00:36<00:11, 26.57 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:36<00:03, 3.40s/ url]
Dl Size...: 80%|███████▉ | 1196/1501 [00:36<00:11, 26.57 MiB/s]
Dl Size...: 80%|███████▉ | 1197/1501 [00:36<00:11, 25.81 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:36<00:03, 3.40s/ url]
Dl Size...: 80%|███████▉ | 1197/1501 [00:36<00:11, 25.81 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:36<00:03, 3.40s/ url]
Dl Size...: 80%|███████▉ | 1198/1501 [00:36<00:11, 25.81 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:36<00:03, 3.40s/ url]
Dl Size...: 80%|███████▉ | 1199/1501 [00:36<00:11, 25.81 MiB/s]
Dl Size...: 80%|███████▉ | 1200/1501 [00:37<00:11, 25.13 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:37<00:03, 3.40s/ url]
Dl Size...: 80%|███████▉ | 1200/1501 [00:37<00:11, 25.13 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:37<00:03, 3.40s/ url]
Dl Size...: 80%|████████ | 1201/1501 [00:37<00:11, 25.13 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:37<00:03, 3.40s/ url]
Dl Size...: 80%|████████ | 1202/1501 [00:37<00:11, 25.13 MiB/s]
Dl Size...: 80%|████████ | 1203/1501 [00:37<00:12, 24.78 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:37<00:03, 3.40s/ url]
Dl Size...: 80%|████████ | 1203/1501 [00:37<00:12, 24.78 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:37<00:03, 3.40s/ url]
Dl Size...: 80%|████████ | 1204/1501 [00:37<00:11, 24.78 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:37<00:03, 3.40s/ url]
Dl Size...: 80%|████████ | 1205/1501 [00:37<00:11, 24.78 MiB/s]
Dl Size...: 80%|████████ | 1206/1501 [00:37<00:11, 25.80 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:37<00:03, 3.40s/ url]
Dl Size...: 80%|████████ | 1206/1501 [00:37<00:11, 25.80 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:37<00:03, 3.40s/ url]
Dl Size...: 80%|████████ | 1207/1501 [00:37<00:11, 25.80 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:37<00:03, 3.40s/ url]
Dl Size...: 80%|████████ | 1208/1501 [00:37<00:11, 25.80 MiB/s]
Dl Size...: 81%|████████ | 1209/1501 [00:37<00:11, 25.08 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:37<00:03, 3.40s/ url]
Dl Size...: 81%|████████ | 1209/1501 [00:37<00:11, 25.08 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:37<00:03, 3.40s/ url]
Dl Size...: 81%|████████ | 1210/1501 [00:37<00:11, 25.08 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:37<00:03, 3.40s/ url]
Dl Size...: 81%|████████ | 1211/1501 [00:37<00:11, 25.08 MiB/s]
Dl Size...: 81%|████████ | 1212/1501 [00:37<00:11, 24.53 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:37<00:03, 3.40s/ url]
Dl Size...: 81%|████████ | 1212/1501 [00:37<00:11, 24.53 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:37<00:03, 3.40s/ url]
Dl Size...: 81%|████████ | 1213/1501 [00:37<00:11, 24.53 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:37<00:03, 3.40s/ url]
Dl Size...: 81%|████████ | 1214/1501 [00:37<00:11, 24.53 MiB/s]
Dl Size...: 81%|████████ | 1215/1501 [00:37<00:11, 25.91 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:37<00:03, 3.40s/ url]
Dl Size...: 81%|████████ | 1215/1501 [00:37<00:11, 25.91 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:37<00:03, 3.40s/ url]
Dl Size...: 81%|████████ | 1216/1501 [00:37<00:11, 25.91 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:37<00:03, 3.40s/ url]
Dl Size...: 81%|████████ | 1217/1501 [00:37<00:10, 25.91 MiB/s]
Dl Size...: 81%|████████ | 1218/1501 [00:37<00:11, 25.11 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:37<00:03, 3.40s/ url]
Dl Size...: 81%|████████ | 1218/1501 [00:37<00:11, 25.11 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:37<00:03, 3.40s/ url]
Dl Size...: 81%|████████ | 1219/1501 [00:37<00:11, 25.11 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:37<00:03, 3.40s/ url]
Dl Size...: 81%|████████▏ | 1220/1501 [00:37<00:11, 25.11 MiB/s]
Dl Size...: 81%|████████▏ | 1221/1501 [00:37<00:11, 24.59 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:37<00:03, 3.40s/ url]
Dl Size...: 81%|████████▏ | 1221/1501 [00:37<00:11, 24.59 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:37<00:03, 3.40s/ url]
Dl Size...: 81%|████████▏ | 1222/1501 [00:37<00:11, 24.59 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:37<00:03, 3.40s/ url]
Dl Size...: 81%|████████▏ | 1223/1501 [00:37<00:11, 24.59 MiB/s]
Dl Size...: 82%|████████▏ | 1224/1501 [00:37<00:10, 25.87 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:37<00:03, 3.40s/ url]
Dl Size...: 82%|████████▏ | 1224/1501 [00:37<00:10, 25.87 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:38<00:03, 3.40s/ url]
Dl Size...: 82%|████████▏ | 1225/1501 [00:38<00:10, 25.87 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:38<00:03, 3.40s/ url]
Dl Size...: 82%|████████▏ | 1226/1501 [00:38<00:10, 25.87 MiB/s]
Dl Size...: 82%|████████▏ | 1227/1501 [00:38<00:10, 25.09 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:38<00:03, 3.40s/ url]
Dl Size...: 82%|████████▏ | 1227/1501 [00:38<00:10, 25.09 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:38<00:03, 3.40s/ url]
Dl Size...: 82%|████████▏ | 1228/1501 [00:38<00:10, 25.09 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:38<00:03, 3.40s/ url]
Dl Size...: 82%|████████▏ | 1229/1501 [00:38<00:10, 25.09 MiB/s]
Dl Size...: 82%|████████▏ | 1230/1501 [00:38<00:11, 24.51 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:38<00:03, 3.40s/ url]
Dl Size...: 82%|████████▏ | 1230/1501 [00:38<00:11, 24.51 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:38<00:03, 3.40s/ url]
Dl Size...: 82%|████████▏ | 1231/1501 [00:38<00:11, 24.51 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:38<00:03, 3.40s/ url]
Dl Size...: 82%|████████▏ | 1232/1501 [00:38<00:10, 24.51 MiB/s]
Dl Size...: 82%|████████▏ | 1233/1501 [00:38<00:11, 24.05 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:38<00:03, 3.40s/ url]
Dl Size...: 82%|████████▏ | 1233/1501 [00:38<00:11, 24.05 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:38<00:03, 3.40s/ url]
Dl Size...: 82%|████████▏ | 1234/1501 [00:38<00:11, 24.05 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:38<00:03, 3.40s/ url]
Dl Size...: 82%|████████▏ | 1235/1501 [00:38<00:11, 24.05 MiB/s]
Dl Size...: 82%|████████▏ | 1236/1501 [00:38<00:10, 25.17 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:38<00:03, 3.40s/ url]
Dl Size...: 82%|████████▏ | 1236/1501 [00:38<00:10, 25.17 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:38<00:03, 3.40s/ url]
Dl Size...: 82%|████████▏ | 1237/1501 [00:38<00:10, 25.17 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:38<00:03, 3.40s/ url]
Dl Size...: 82%|████████▏ | 1238/1501 [00:38<00:10, 25.17 MiB/s]
Dl Size...: 83%|████████▎ | 1239/1501 [00:38<00:10, 24.73 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:38<00:03, 3.40s/ url]
Dl Size...: 83%|████████▎ | 1239/1501 [00:38<00:10, 24.73 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:38<00:03, 3.40s/ url]
Dl Size...: 83%|████████▎ | 1240/1501 [00:38<00:10, 24.73 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:38<00:03, 3.40s/ url]
Dl Size...: 83%|████████▎ | 1241/1501 [00:38<00:10, 24.73 MiB/s]
Dl Size...: 83%|████████▎ | 1242/1501 [00:38<00:10, 24.32 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:38<00:03, 3.40s/ url]
Dl Size...: 83%|████████▎ | 1242/1501 [00:38<00:10, 24.32 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:38<00:03, 3.40s/ url]
Dl Size...: 83%|████████▎ | 1243/1501 [00:38<00:10, 24.32 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:38<00:03, 3.40s/ url]
Dl Size...: 83%|████████▎ | 1244/1501 [00:38<00:10, 24.32 MiB/s]
Dl Size...: 83%|████████▎ | 1245/1501 [00:38<00:09, 25.71 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:38<00:03, 3.40s/ url]
Dl Size...: 83%|████████▎ | 1245/1501 [00:38<00:09, 25.71 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:38<00:03, 3.40s/ url]
Dl Size...: 83%|████████▎ | 1246/1501 [00:38<00:09, 25.71 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:38<00:03, 3.40s/ url]
Dl Size...: 83%|████████▎ | 1247/1501 [00:38<00:09, 25.71 MiB/s]
Dl Size...: 83%|████████▎ | 1248/1501 [00:38<00:10, 25.21 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:38<00:03, 3.40s/ url]
Dl Size...: 83%|████████▎ | 1248/1501 [00:38<00:10, 25.21 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:38<00:03, 3.40s/ url]
Dl Size...: 83%|████████▎ | 1249/1501 [00:38<00:09, 25.21 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:39<00:03, 3.40s/ url]
Dl Size...: 83%|████████▎ | 1250/1501 [00:39<00:09, 25.21 MiB/s]
Dl Size...: 83%|████████▎ | 1251/1501 [00:39<00:10, 24.59 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:39<00:03, 3.40s/ url]
Dl Size...: 83%|████████▎ | 1251/1501 [00:39<00:10, 24.59 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:39<00:03, 3.40s/ url]
Dl Size...: 83%|████████▎ | 1252/1501 [00:39<00:10, 24.59 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:39<00:03, 3.40s/ url]
Dl Size...: 83%|████████▎ | 1253/1501 [00:39<00:10, 24.59 MiB/s]
Dl Size...: 84%|████████▎ | 1254/1501 [00:39<00:10, 24.51 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:39<00:03, 3.40s/ url]
Dl Size...: 84%|████████▎ | 1254/1501 [00:39<00:10, 24.51 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:39<00:03, 3.40s/ url]
Dl Size...: 84%|████████▎ | 1255/1501 [00:39<00:10, 24.51 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:39<00:03, 3.40s/ url]
Dl Size...: 84%|████████▎ | 1256/1501 [00:39<00:09, 24.51 MiB/s]
Dl Size...: 84%|████████▎ | 1257/1501 [00:39<00:09, 25.56 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:39<00:03, 3.40s/ url]
Dl Size...: 84%|████████▎ | 1257/1501 [00:39<00:09, 25.56 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:39<00:03, 3.40s/ url]
Dl Size...: 84%|████████▍ | 1258/1501 [00:39<00:09, 25.56 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:39<00:03, 3.40s/ url]
Dl Size...: 84%|████████▍ | 1259/1501 [00:39<00:09, 25.56 MiB/s]
Dl Size...: 84%|████████▍ | 1260/1501 [00:39<00:09, 24.92 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:39<00:03, 3.40s/ url]
Dl Size...: 84%|████████▍ | 1260/1501 [00:39<00:09, 24.92 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:39<00:03, 3.40s/ url]
Dl Size...: 84%|████████▍ | 1261/1501 [00:39<00:09, 24.92 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:39<00:03, 3.40s/ url]
Dl Size...: 84%|████████▍ | 1262/1501 [00:39<00:09, 24.92 MiB/s]
Dl Size...: 84%|████████▍ | 1263/1501 [00:39<00:09, 24.25 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:39<00:03, 3.40s/ url]
Dl Size...: 84%|████████▍ | 1263/1501 [00:39<00:09, 24.25 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:39<00:03, 3.40s/ url]
Dl Size...: 84%|████████▍ | 1264/1501 [00:39<00:09, 24.25 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:39<00:03, 3.40s/ url]
Dl Size...: 84%|████████▍ | 1265/1501 [00:39<00:09, 24.25 MiB/s]
Dl Size...: 84%|████████▍ | 1266/1501 [00:39<00:09, 25.47 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:39<00:03, 3.40s/ url]
Dl Size...: 84%|████████▍ | 1266/1501 [00:39<00:09, 25.47 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:39<00:03, 3.40s/ url]
Dl Size...: 84%|████████▍ | 1267/1501 [00:39<00:09, 25.47 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:39<00:03, 3.40s/ url]
Dl Size...: 84%|████████▍ | 1268/1501 [00:39<00:09, 25.47 MiB/s]
Dl Size...: 85%|████████▍ | 1269/1501 [00:39<00:09, 25.03 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:39<00:03, 3.40s/ url]
Dl Size...: 85%|████████▍ | 1269/1501 [00:39<00:09, 25.03 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:39<00:03, 3.40s/ url]
Dl Size...: 85%|████████▍ | 1270/1501 [00:39<00:09, 25.03 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:39<00:03, 3.40s/ url]
Dl Size...: 85%|████████▍ | 1271/1501 [00:39<00:09, 25.03 MiB/s]
Dl Size...: 85%|████████▍ | 1272/1501 [00:39<00:09, 24.80 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:39<00:03, 3.40s/ url]
Dl Size...: 85%|████████▍ | 1272/1501 [00:39<00:09, 24.80 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:39<00:03, 3.40s/ url]
Dl Size...: 85%|████████▍ | 1273/1501 [00:39<00:09, 24.80 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:39<00:03, 3.40s/ url]
Dl Size...: 85%|████████▍ | 1274/1501 [00:39<00:09, 24.80 MiB/s]
Dl Size...: 85%|████████▍ | 1275/1501 [00:40<00:09, 24.52 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:40<00:03, 3.40s/ url]
Dl Size...: 85%|████████▍ | 1275/1501 [00:40<00:09, 24.52 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:40<00:03, 3.40s/ url]
Dl Size...: 85%|████████▌ | 1276/1501 [00:40<00:09, 24.52 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:40<00:03, 3.40s/ url]
Dl Size...: 85%|████████▌ | 1277/1501 [00:40<00:09, 24.52 MiB/s]
Dl Size...: 85%|████████▌ | 1278/1501 [00:40<00:08, 25.70 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:40<00:03, 3.40s/ url]
Dl Size...: 85%|████████▌ | 1278/1501 [00:40<00:08, 25.70 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:40<00:03, 3.40s/ url]
Dl Size...: 85%|████████▌ | 1279/1501 [00:40<00:08, 25.70 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:40<00:03, 3.40s/ url]
Dl Size...: 85%|████████▌ | 1280/1501 [00:40<00:08, 25.70 MiB/s]
Dl Size...: 85%|████████▌ | 1281/1501 [00:40<00:08, 25.03 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:40<00:03, 3.40s/ url]
Dl Size...: 85%|████████▌ | 1281/1501 [00:40<00:08, 25.03 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:40<00:03, 3.40s/ url]
Dl Size...: 85%|████████▌ | 1282/1501 [00:40<00:08, 25.03 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:40<00:03, 3.40s/ url]
Dl Size...: 85%|████████▌ | 1283/1501 [00:40<00:08, 25.03 MiB/s]
Dl Size...: 86%|████████▌ | 1284/1501 [00:40<00:08, 24.84 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:40<00:03, 3.40s/ url]
Dl Size...: 86%|████████▌ | 1284/1501 [00:40<00:08, 24.84 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:40<00:03, 3.40s/ url]
Dl Size...: 86%|████████▌ | 1285/1501 [00:40<00:08, 24.84 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:40<00:03, 3.40s/ url]
Dl Size...: 86%|████████▌ | 1286/1501 [00:40<00:08, 24.84 MiB/s]
Dl Size...: 86%|████████▌ | 1287/1501 [00:40<00:08, 26.13 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:40<00:03, 3.40s/ url]
Dl Size...: 86%|████████▌ | 1287/1501 [00:40<00:08, 26.13 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:40<00:03, 3.40s/ url]
Dl Size...: 86%|████████▌ | 1288/1501 [00:40<00:08, 26.13 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:40<00:03, 3.40s/ url]
Dl Size...: 86%|████████▌ | 1289/1501 [00:40<00:08, 26.13 MiB/s]
Dl Size...: 86%|████████▌ | 1290/1501 [00:40<00:08, 25.66 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:40<00:03, 3.40s/ url]
Dl Size...: 86%|████████▌ | 1290/1501 [00:40<00:08, 25.66 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:40<00:03, 3.40s/ url]
Dl Size...: 86%|████████▌ | 1291/1501 [00:40<00:08, 25.66 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:40<00:03, 3.40s/ url]
Dl Size...: 86%|████████▌ | 1292/1501 [00:40<00:08, 25.66 MiB/s]
Dl Size...: 86%|████████▌ | 1293/1501 [00:40<00:08, 24.96 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:40<00:03, 3.40s/ url]
Dl Size...: 86%|████████▌ | 1293/1501 [00:40<00:08, 24.96 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:40<00:03, 3.40s/ url]
Dl Size...: 86%|████████▌ | 1294/1501 [00:40<00:08, 24.96 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:40<00:03, 3.40s/ url]
Dl Size...: 86%|████████▋ | 1295/1501 [00:40<00:08, 24.96 MiB/s]
Dl Size...: 86%|████████▋ | 1296/1501 [00:40<00:07, 26.20 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:40<00:03, 3.40s/ url]
Dl Size...: 86%|████████▋ | 1296/1501 [00:40<00:07, 26.20 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:40<00:03, 3.40s/ url]
Dl Size...: 86%|████████▋ | 1297/1501 [00:40<00:07, 26.20 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:40<00:03, 3.40s/ url]
Dl Size...: 86%|████████▋ | 1298/1501 [00:40<00:07, 26.20 MiB/s]
Dl Size...: 87%|████████▋ | 1299/1501 [00:40<00:07, 25.75 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:40<00:03, 3.40s/ url]
Dl Size...: 87%|████████▋ | 1299/1501 [00:40<00:07, 25.75 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:41<00:03, 3.40s/ url]
Dl Size...: 87%|████████▋ | 1300/1501 [00:41<00:07, 25.75 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:41<00:03, 3.40s/ url]
Dl Size...: 87%|████████▋ | 1301/1501 [00:41<00:07, 25.75 MiB/s]
Dl Size...: 87%|████████▋ | 1302/1501 [00:41<00:07, 25.20 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:41<00:03, 3.40s/ url]
Dl Size...: 87%|████████▋ | 1302/1501 [00:41<00:07, 25.20 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:41<00:03, 3.40s/ url]
Dl Size...: 87%|████████▋ | 1303/1501 [00:41<00:07, 25.20 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:41<00:03, 3.40s/ url]
Dl Size...: 87%|████████▋ | 1304/1501 [00:41<00:07, 25.20 MiB/s]
Dl Size...: 87%|████████▋ | 1305/1501 [00:41<00:07, 25.56 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:41<00:03, 3.40s/ url]
Dl Size...: 87%|████████▋ | 1305/1501 [00:41<00:07, 25.56 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:41<00:03, 3.40s/ url]
Dl Size...: 87%|████████▋ | 1306/1501 [00:41<00:07, 25.56 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:41<00:03, 3.40s/ url]
Dl Size...: 87%|████████▋ | 1307/1501 [00:41<00:07, 25.56 MiB/s]
Dl Size...: 87%|████████▋ | 1308/1501 [00:41<00:07, 25.78 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:41<00:03, 3.40s/ url]
Dl Size...: 87%|████████▋ | 1308/1501 [00:41<00:07, 25.78 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:41<00:03, 3.40s/ url]
Dl Size...: 87%|████████▋ | 1309/1501 [00:41<00:07, 25.78 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:41<00:03, 3.40s/ url]
Dl Size...: 87%|████████▋ | 1310/1501 [00:41<00:07, 25.78 MiB/s]
Dl Size...: 87%|████████▋ | 1311/1501 [00:41<00:07, 25.39 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:41<00:03, 3.40s/ url]
Dl Size...: 87%|████████▋ | 1311/1501 [00:41<00:07, 25.39 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:41<00:03, 3.40s/ url]
Dl Size...: 87%|████████▋ | 1312/1501 [00:41<00:07, 25.39 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:41<00:03, 3.40s/ url]
Dl Size...: 87%|████████▋ | 1313/1501 [00:41<00:07, 25.39 MiB/s]
Dl Size...: 88%|████████▊ | 1314/1501 [00:41<00:07, 24.17 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:41<00:03, 3.40s/ url]
Dl Size...: 88%|████████▊ | 1314/1501 [00:41<00:07, 24.17 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:41<00:03, 3.40s/ url]
Dl Size...: 88%|████████▊ | 1315/1501 [00:41<00:07, 24.17 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:41<00:03, 3.40s/ url]
Dl Size...: 88%|████████▊ | 1316/1501 [00:41<00:07, 24.17 MiB/s]
Dl Size...: 88%|████████▊ | 1317/1501 [00:41<00:07, 25.47 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:41<00:03, 3.40s/ url]
Dl Size...: 88%|████████▊ | 1317/1501 [00:41<00:07, 25.47 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:41<00:03, 3.40s/ url]
Dl Size...: 88%|████████▊ | 1318/1501 [00:41<00:07, 25.47 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:41<00:03, 3.40s/ url]
Dl Size...: 88%|████████▊ | 1319/1501 [00:41<00:07, 25.47 MiB/s]
Dl Size...: 88%|████████▊ | 1320/1501 [00:41<00:07, 24.97 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:41<00:03, 3.40s/ url]
Dl Size...: 88%|████████▊ | 1320/1501 [00:41<00:07, 24.97 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:41<00:03, 3.40s/ url]
Dl Size...: 88%|████████▊ | 1321/1501 [00:41<00:07, 24.97 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:41<00:03, 3.40s/ url]
Dl Size...: 88%|████████▊ | 1322/1501 [00:41<00:07, 24.97 MiB/s]
Dl Size...: 88%|████████▊ | 1323/1501 [00:41<00:07, 24.50 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:41<00:03, 3.40s/ url]
Dl Size...: 88%|████████▊ | 1323/1501 [00:41<00:07, 24.50 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:41<00:03, 3.40s/ url]
Dl Size...: 88%|████████▊ | 1324/1501 [00:41<00:07, 24.50 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:42<00:03, 3.40s/ url]
Dl Size...: 88%|████████▊ | 1325/1501 [00:42<00:07, 24.50 MiB/s]
Dl Size...: 88%|████████▊ | 1326/1501 [00:42<00:06, 25.87 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:42<00:03, 3.40s/ url]
Dl Size...: 88%|████████▊ | 1326/1501 [00:42<00:06, 25.87 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:42<00:03, 3.40s/ url]
Dl Size...: 88%|████████▊ | 1327/1501 [00:42<00:06, 25.87 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:42<00:03, 3.40s/ url]
Dl Size...: 88%|████████▊ | 1328/1501 [00:42<00:06, 25.87 MiB/s]
Dl Size...: 89%|████████▊ | 1329/1501 [00:42<00:06, 25.27 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:42<00:03, 3.40s/ url]
Dl Size...: 89%|████████▊ | 1329/1501 [00:42<00:06, 25.27 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:42<00:03, 3.40s/ url]
Dl Size...: 89%|████████▊ | 1330/1501 [00:42<00:06, 25.27 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:42<00:03, 3.40s/ url]
Dl Size...: 89%|████████▊ | 1331/1501 [00:42<00:06, 25.27 MiB/s]
Dl Size...: 89%|████████▊ | 1332/1501 [00:42<00:06, 24.70 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:42<00:03, 3.40s/ url]
Dl Size...: 89%|████████▊ | 1332/1501 [00:42<00:06, 24.70 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:42<00:03, 3.40s/ url]
Dl Size...: 89%|████████▉ | 1333/1501 [00:42<00:06, 24.70 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:42<00:03, 3.40s/ url]
Dl Size...: 89%|████████▉ | 1334/1501 [00:42<00:06, 24.70 MiB/s]
Dl Size...: 89%|████████▉ | 1335/1501 [00:42<00:06, 24.63 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:42<00:03, 3.40s/ url]
Dl Size...: 89%|████████▉ | 1335/1501 [00:42<00:06, 24.63 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:42<00:03, 3.40s/ url]
Dl Size...: 89%|████████▉ | 1336/1501 [00:42<00:06, 24.63 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:42<00:03, 3.40s/ url]
Dl Size...: 89%|████████▉ | 1337/1501 [00:42<00:06, 24.63 MiB/s]
Dl Size...: 89%|████████▉ | 1338/1501 [00:42<00:06, 25.73 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:42<00:03, 3.40s/ url]
Dl Size...: 89%|████████▉ | 1338/1501 [00:42<00:06, 25.73 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:42<00:03, 3.40s/ url]
Dl Size...: 89%|████████▉ | 1339/1501 [00:42<00:06, 25.73 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:42<00:03, 3.40s/ url]
Dl Size...: 89%|████████▉ | 1340/1501 [00:42<00:06, 25.73 MiB/s]
Dl Size...: 89%|████████▉ | 1341/1501 [00:42<00:06, 25.26 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:42<00:03, 3.40s/ url]
Dl Size...: 89%|████████▉ | 1341/1501 [00:42<00:06, 25.26 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:42<00:03, 3.40s/ url]
Dl Size...: 89%|████████▉ | 1342/1501 [00:42<00:06, 25.26 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:42<00:03, 3.40s/ url]
Dl Size...: 89%|████████▉ | 1343/1501 [00:42<00:06, 25.26 MiB/s]
Dl Size...: 90%|████████▉ | 1344/1501 [00:42<00:06, 24.63 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:42<00:03, 3.40s/ url]
Dl Size...: 90%|████████▉ | 1344/1501 [00:42<00:06, 24.63 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:42<00:03, 3.40s/ url]
Dl Size...: 90%|████████▉ | 1345/1501 [00:42<00:06, 24.63 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:42<00:03, 3.40s/ url]
Dl Size...: 90%|████████▉ | 1346/1501 [00:42<00:06, 24.63 MiB/s]
Dl Size...: 90%|████████▉ | 1347/1501 [00:42<00:05, 26.00 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:42<00:03, 3.40s/ url]
Dl Size...: 90%|████████▉ | 1347/1501 [00:42<00:05, 26.00 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:42<00:03, 3.40s/ url]
Dl Size...: 90%|████████▉ | 1348/1501 [00:42<00:05, 26.00 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:42<00:03, 3.40s/ url]
Dl Size...: 90%|████████▉ | 1349/1501 [00:42<00:05, 26.00 MiB/s]
Dl Size...: 90%|████████▉ | 1350/1501 [00:42<00:05, 25.51 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:42<00:03, 3.40s/ url]
Dl Size...: 90%|████████▉ | 1350/1501 [00:42<00:05, 25.51 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:43<00:03, 3.40s/ url]
Dl Size...: 90%|█████████ | 1351/1501 [00:43<00:05, 25.51 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:43<00:03, 3.40s/ url]
Dl Size...: 90%|█████████ | 1352/1501 [00:43<00:05, 25.51 MiB/s]
Dl Size...: 90%|█████████ | 1353/1501 [00:43<00:05, 24.76 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:43<00:03, 3.40s/ url]
Dl Size...: 90%|█████████ | 1353/1501 [00:43<00:05, 24.76 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:43<00:03, 3.40s/ url]
Dl Size...: 90%|█████████ | 1354/1501 [00:43<00:05, 24.76 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:43<00:03, 3.40s/ url]
Dl Size...: 90%|█████████ | 1355/1501 [00:43<00:05, 24.76 MiB/s]
Dl Size...: 90%|█████████ | 1356/1501 [00:43<00:05, 26.13 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:43<00:03, 3.40s/ url]
Dl Size...: 90%|█████████ | 1356/1501 [00:43<00:05, 26.13 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:43<00:03, 3.40s/ url]
Dl Size...: 90%|█████████ | 1357/1501 [00:43<00:05, 26.13 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:43<00:03, 3.40s/ url]
Dl Size...: 90%|█████████ | 1358/1501 [00:43<00:05, 26.13 MiB/s]
Dl Size...: 91%|█████████ | 1359/1501 [00:43<00:05, 25.61 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:43<00:03, 3.40s/ url]
Dl Size...: 91%|█████████ | 1359/1501 [00:43<00:05, 25.61 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:43<00:03, 3.40s/ url]
Dl Size...: 91%|█████████ | 1360/1501 [00:43<00:05, 25.61 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:43<00:03, 3.40s/ url]
Dl Size...: 91%|█████████ | 1361/1501 [00:43<00:05, 25.61 MiB/s]
Dl Size...: 91%|█████████ | 1362/1501 [00:43<00:05, 24.75 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:43<00:03, 3.40s/ url]
Dl Size...: 91%|█████████ | 1362/1501 [00:43<00:05, 24.75 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:43<00:03, 3.40s/ url]
Dl Size...: 91%|█████████ | 1363/1501 [00:43<00:05, 24.75 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:43<00:03, 3.40s/ url]
Dl Size...: 91%|█████████ | 1364/1501 [00:43<00:05, 24.75 MiB/s]
Dl Size...: 91%|█████████ | 1365/1501 [00:43<00:09, 15.10 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:43<00:03, 3.40s/ url]
Dl Size...: 91%|█████████ | 1365/1501 [00:43<00:09, 15.10 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:43<00:03, 3.40s/ url]
Dl Size...: 91%|█████████ | 1366/1501 [00:43<00:08, 15.10 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:43<00:03, 3.40s/ url]
Dl Size...: 91%|█████████ | 1367/1501 [00:43<00:08, 15.10 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:43<00:03, 3.40s/ url]
Dl Size...: 91%|█████████ | 1368/1501 [00:43<00:08, 15.10 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:43<00:03, 3.40s/ url]
Dl Size...: 91%|█████████ | 1369/1501 [00:43<00:08, 15.10 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:43<00:03, 3.40s/ url]
Dl Size...: 91%|█████████▏| 1370/1501 [00:43<00:08, 15.10 MiB/s]
Dl Size...: 91%|█████████▏| 1371/1501 [00:43<00:05, 22.20 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:43<00:03, 3.40s/ url]
Dl Size...: 91%|█████████▏| 1371/1501 [00:43<00:05, 22.20 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:44<00:03, 3.40s/ url]
Dl Size...: 91%|█████████▏| 1372/1501 [00:44<00:05, 22.20 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:44<00:03, 3.40s/ url]
Dl Size...: 91%|█████████▏| 1373/1501 [00:44<00:05, 22.20 MiB/s]
Dl Size...: 92%|█████████▏| 1374/1501 [00:44<00:05, 22.69 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:44<00:03, 3.40s/ url]
Dl Size...: 92%|█████████▏| 1374/1501 [00:44<00:05, 22.69 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:44<00:03, 3.40s/ url]
Dl Size...: 92%|█████████▏| 1375/1501 [00:44<00:05, 22.69 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:44<00:03, 3.40s/ url]
Dl Size...: 92%|█████████▏| 1376/1501 [00:44<00:05, 22.69 MiB/s]
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Dl Completed...: 67%|██████▋ | 2/3 [00:44<00:03, 3.40s/ url]
Dl Size...: 92%|█████████▏| 1377/1501 [00:44<00:05, 24.15 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:44<00:03, 3.40s/ url]
Dl Size...: 92%|█████████▏| 1378/1501 [00:44<00:05, 24.15 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:44<00:03, 3.40s/ url]
Dl Size...: 92%|█████████▏| 1379/1501 [00:44<00:05, 24.15 MiB/s]
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Dl Completed...: 67%|██████▋ | 2/3 [00:44<00:03, 3.40s/ url]
Dl Size...: 92%|█████████▏| 1380/1501 [00:44<00:05, 23.85 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:44<00:03, 3.40s/ url]
Dl Size...: 92%|█████████▏| 1381/1501 [00:44<00:05, 23.85 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:44<00:03, 3.40s/ url]
Dl Size...: 92%|█████████▏| 1382/1501 [00:44<00:04, 23.85 MiB/s]
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Dl Completed...: 67%|██████▋ | 2/3 [00:44<00:03, 3.40s/ url]
Dl Size...: 92%|█████████▏| 1383/1501 [00:44<00:04, 24.17 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:44<00:03, 3.40s/ url]
Dl Size...: 92%|█████████▏| 1384/1501 [00:44<00:04, 24.17 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:44<00:03, 3.40s/ url]
Dl Size...: 92%|█████████▏| 1385/1501 [00:44<00:04, 24.17 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:44<00:03, 3.40s/ url]
Dl Size...: 92%|█████████▏| 1386/1501 [00:44<00:04, 24.17 MiB/s]
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Dl Completed...: 67%|██████▋ | 2/3 [00:44<00:03, 3.40s/ url]
Dl Size...: 92%|█████████▏| 1387/1501 [00:44<00:04, 24.61 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:44<00:03, 3.40s/ url]
Dl Size...: 92%|█████████▏| 1388/1501 [00:44<00:04, 24.61 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:44<00:03, 3.40s/ url]
Dl Size...: 93%|█████████▎| 1389/1501 [00:44<00:04, 24.61 MiB/s]
Dl Size...: 93%|█████████▎| 1390/1501 [00:44<00:04, 25.37 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:44<00:03, 3.40s/ url]
Dl Size...: 93%|█████████▎| 1390/1501 [00:44<00:04, 25.37 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:44<00:03, 3.40s/ url]
Dl Size...: 93%|█████████▎| 1391/1501 [00:44<00:04, 25.37 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:44<00:03, 3.40s/ url]
Dl Size...: 93%|█████████▎| 1392/1501 [00:44<00:04, 25.37 MiB/s]
Dl Size...: 93%|█████████▎| 1393/1501 [00:44<00:04, 25.12 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:44<00:03, 3.40s/ url]
Dl Size...: 93%|█████████▎| 1393/1501 [00:44<00:04, 25.12 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:44<00:03, 3.40s/ url]
Dl Size...: 93%|█████████▎| 1394/1501 [00:44<00:04, 25.12 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:44<00:03, 3.40s/ url]
Dl Size...: 93%|█████████▎| 1395/1501 [00:44<00:04, 25.12 MiB/s]
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Dl Completed...: 67%|██████▋ | 2/3 [00:44<00:03, 3.40s/ url]
Dl Size...: 93%|█████████▎| 1396/1501 [00:44<00:04, 24.54 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:45<00:03, 3.40s/ url]
Dl Size...: 93%|█████████▎| 1397/1501 [00:45<00:04, 24.54 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:45<00:03, 3.40s/ url]
Dl Size...: 93%|█████████▎| 1398/1501 [00:45<00:04, 24.54 MiB/s]
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Dl Completed...: 67%|██████▋ | 2/3 [00:45<00:03, 3.40s/ url]
Dl Size...: 93%|█████████▎| 1399/1501 [00:45<00:03, 25.58 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:45<00:03, 3.40s/ url]
Dl Size...: 93%|█████████▎| 1400/1501 [00:45<00:03, 25.58 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:45<00:03, 3.40s/ url]
Dl Size...: 93%|█████████▎| 1401/1501 [00:45<00:03, 25.58 MiB/s]
Dl Size...: 93%|█████████▎| 1402/1501 [00:45<00:03, 25.03 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:45<00:03, 3.40s/ url]
Dl Size...: 93%|█████████▎| 1402/1501 [00:45<00:03, 25.03 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:45<00:03, 3.40s/ url]
Dl Size...: 93%|█████████▎| 1403/1501 [00:45<00:03, 25.03 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:45<00:03, 3.40s/ url]
Dl Size...: 94%|█████████▎| 1404/1501 [00:45<00:03, 25.03 MiB/s]
Dl Size...: 94%|█████████▎| 1405/1501 [00:45<00:03, 24.90 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:45<00:03, 3.40s/ url]
Dl Size...: 94%|█████████▎| 1405/1501 [00:45<00:03, 24.90 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:45<00:03, 3.40s/ url]
Dl Size...: 94%|█████████▎| 1406/1501 [00:45<00:03, 24.90 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:45<00:03, 3.40s/ url]
Dl Size...: 94%|█████████▎| 1407/1501 [00:45<00:03, 24.90 MiB/s]
Dl Size...: 94%|█████████▍| 1408/1501 [00:45<00:03, 24.66 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:45<00:03, 3.40s/ url]
Dl Size...: 94%|█████████▍| 1408/1501 [00:45<00:03, 24.66 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:45<00:03, 3.40s/ url]
Dl Size...: 94%|█████████▍| 1409/1501 [00:45<00:03, 24.66 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:45<00:03, 3.40s/ url]
Dl Size...: 94%|█████████▍| 1410/1501 [00:45<00:03, 24.66 MiB/s]
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Dl Completed...: 67%|██████▋ | 2/3 [00:45<00:03, 3.40s/ url]
Dl Size...: 94%|█████████▍| 1411/1501 [00:45<00:03, 25.48 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:45<00:03, 3.40s/ url]
Dl Size...: 94%|█████████▍| 1412/1501 [00:45<00:03, 25.48 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:45<00:03, 3.40s/ url]
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Dl Size...: 94%|█████████▍| 1414/1501 [00:45<00:03, 24.97 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:45<00:03, 3.40s/ url]
Dl Size...: 94%|█████████▍| 1414/1501 [00:45<00:03, 24.97 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:45<00:03, 3.40s/ url]
Dl Size...: 94%|█████████▍| 1415/1501 [00:45<00:03, 24.97 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:45<00:03, 3.40s/ url]
Dl Size...: 94%|█████████▍| 1416/1501 [00:45<00:03, 24.97 MiB/s]
Dl Size...: 94%|█████████▍| 1417/1501 [00:45<00:03, 24.79 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:45<00:03, 3.40s/ url]
Dl Size...: 94%|█████████▍| 1417/1501 [00:45<00:03, 24.79 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:45<00:03, 3.40s/ url]
Dl Size...: 94%|█████████▍| 1418/1501 [00:45<00:03, 24.79 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:45<00:03, 3.40s/ url]
Dl Size...: 95%|█████████▍| 1419/1501 [00:45<00:03, 24.79 MiB/s]
Dl Size...: 95%|█████████▍| 1420/1501 [00:45<00:03, 25.75 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:45<00:03, 3.40s/ url]
Dl Size...: 95%|█████████▍| 1420/1501 [00:45<00:03, 25.75 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:45<00:03, 3.40s/ url]
Dl Size...: 95%|█████████▍| 1421/1501 [00:45<00:03, 25.75 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:46<00:03, 3.40s/ url]
Dl Size...: 95%|█████████▍| 1422/1501 [00:45<00:03, 25.75 MiB/s]
Dl Size...: 95%|█████████▍| 1423/1501 [00:46<00:03, 25.09 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:46<00:03, 3.40s/ url]
Dl Size...: 95%|█████████▍| 1423/1501 [00:46<00:03, 25.09 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:46<00:03, 3.40s/ url]
Dl Size...: 95%|█████████▍| 1424/1501 [00:46<00:03, 25.09 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:46<00:03, 3.40s/ url]
Dl Size...: 95%|█████████▍| 1425/1501 [00:46<00:03, 25.09 MiB/s]
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Dl Completed...: 67%|██████▋ | 2/3 [00:46<00:03, 3.40s/ url]
Dl Size...: 95%|█████████▌| 1426/1501 [00:46<00:03, 24.95 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:46<00:03, 3.40s/ url]
Dl Size...: 95%|█████████▌| 1427/1501 [00:46<00:02, 24.95 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:46<00:03, 3.40s/ url]
Dl Size...: 95%|█████████▌| 1428/1501 [00:46<00:02, 24.95 MiB/s]
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Dl Completed...: 67%|██████▋ | 2/3 [00:46<00:03, 3.40s/ url]
Dl Size...: 95%|█████████▌| 1429/1501 [00:46<00:02, 25.89 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:46<00:03, 3.40s/ url]
Dl Size...: 95%|█████████▌| 1430/1501 [00:46<00:02, 25.89 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:46<00:03, 3.40s/ url]
Dl Size...: 95%|█████████▌| 1431/1501 [00:46<00:02, 25.89 MiB/s]
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Dl Completed...: 67%|██████▋ | 2/3 [00:46<00:03, 3.40s/ url]
Dl Size...: 95%|█████████▌| 1432/1501 [00:46<00:02, 25.30 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:46<00:03, 3.40s/ url]
Dl Size...: 95%|█████████▌| 1433/1501 [00:46<00:02, 25.30 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:46<00:03, 3.40s/ url]
Dl Size...: 96%|█████████▌| 1434/1501 [00:46<00:02, 25.30 MiB/s]
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Dl Completed...: 67%|██████▋ | 2/3 [00:46<00:03, 3.40s/ url]
Dl Size...: 96%|█████████▌| 1435/1501 [00:46<00:02, 24.94 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:46<00:03, 3.40s/ url]
Dl Size...: 96%|█████████▌| 1436/1501 [00:46<00:02, 24.94 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:46<00:03, 3.40s/ url]
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Dl Completed...: 67%|██████▋ | 2/3 [00:46<00:03, 3.40s/ url]
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Dl Completed...: 67%|██████▋ | 2/3 [00:46<00:03, 3.40s/ url]
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Dl Completed...: 67%|██████▋ | 2/3 [00:46<00:03, 3.40s/ url]
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Dl Completed...: 67%|██████▋ | 2/3 [00:46<00:03, 3.40s/ url]
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Dl Completed...: 67%|██████▋ | 2/3 [00:46<00:03, 3.40s/ url]
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Dl Completed...: 67%|██████▋ | 2/3 [00:46<00:03, 3.40s/ url]
Dl Size...: 96%|█████████▌| 1444/1501 [00:46<00:02, 25.07 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:46<00:03, 3.40s/ url]
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Dl Completed...: 67%|██████▋ | 2/3 [00:46<00:03, 3.40s/ url]
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Dl Completed...: 67%|██████▋ | 2/3 [00:47<00:03, 3.40s/ url]
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Dl Completed...: 67%|██████▋ | 2/3 [00:47<00:03, 3.40s/ url]
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Dl Completed...: 67%|██████▋ | 2/3 [00:47<00:03, 3.40s/ url]
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Dl Completed...: 67%|██████▋ | 2/3 [00:47<00:03, 3.40s/ url]
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Dl Completed...: 67%|██████▋ | 2/3 [00:47<00:03, 3.40s/ url]
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Dl Completed...: 67%|██████▋ | 2/3 [00:47<00:03, 3.40s/ url]
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Dl Completed...: 67%|██████▋ | 2/3 [00:47<00:03, 3.40s/ url]
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Dl Completed...: 67%|██████▋ | 2/3 [00:47<00:03, 3.40s/ url]
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Dl Completed...: 67%|██████▋ | 2/3 [00:47<00:03, 3.40s/ url]
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Dl Completed...: 67%|██████▋ | 2/3 [00:47<00:03, 3.40s/ url]
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Dl Completed...: 67%|██████▋ | 2/3 [00:47<00:03, 3.40s/ url]
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Dl Completed...: 67%|██████▋ | 2/3 [00:47<00:03, 3.40s/ url]
Dl Size...: 97%|█████████▋| 1460/1501 [00:47<00:01, 24.33 MiB/s]
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Dl Completed...: 67%|██████▋ | 2/3 [00:47<00:03, 3.40s/ url]
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Dl Completed...: 67%|██████▋ | 2/3 [00:47<00:03, 3.40s/ url]
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Dl Completed...: 67%|██████▋ | 2/3 [00:47<00:03, 3.40s/ url]
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Generating splits...: 0%| | 0/3 [00:00<?, ? splits/s]
Generating train examples...: 0 examples [00:00, ? examples/s]
2023-06-24 05:59:32.281759: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: SSE4.1 SSE4.2 AVX AVX2 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
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Shuffling /home/runner/tensorflow_datasets/svhn_cropped/3.0.0.incompleteI408JH/svhn_cropped-train.tfrecord*...: 0%| | 0/73257 [00:00<?, ? examples/s]
Shuffling /home/runner/tensorflow_datasets/svhn_cropped/3.0.0.incompleteI408JH/svhn_cropped-train.tfrecord*...: 14%|█▍ | 10569/73257 [00:00<00:00, 105676.50 examples/s]
Shuffling /home/runner/tensorflow_datasets/svhn_cropped/3.0.0.incompleteI408JH/svhn_cropped-train.tfrecord*...: 39%|███▉ | 28821/73257 [00:00<00:00, 150872.03 examples/s]
Shuffling /home/runner/tensorflow_datasets/svhn_cropped/3.0.0.incompleteI408JH/svhn_cropped-train.tfrecord*...: 65%|██████▍ | 47364/73257 [00:00<00:00, 166638.79 examples/s]
Shuffling /home/runner/tensorflow_datasets/svhn_cropped/3.0.0.incompleteI408JH/svhn_cropped-train.tfrecord*...: 90%|█████████ | 66164/73257 [00:00<00:00, 175064.67 examples/s]
Generating splits...: 33%|███▎ | 1/3 [00:44<01:29, 44.66s/ splits]
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Shuffling /home/runner/tensorflow_datasets/svhn_cropped/3.0.0.incompleteI408JH/svhn_cropped-test.tfrecord*...: 0%| | 0/26032 [00:00<?, ? examples/s]
Shuffling /home/runner/tensorflow_datasets/svhn_cropped/3.0.0.incompleteI408JH/svhn_cropped-test.tfrecord*...: 65%|██████▌ | 16926/26032 [00:00<00:00, 169244.02 examples/s]
Generating splits...: 67%|██████▋ | 2/3 [01:00<00:27, 27.87s/ splits]
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Shuffling /home/runner/tensorflow_datasets/svhn_cropped/3.0.0.incompleteI408JH/svhn_cropped-extra.tfrecord*...: 0%| | 0/531131 [00:00<?, ? examples/s]
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Shuffling /home/runner/tensorflow_datasets/svhn_cropped/3.0.0.incompleteI408JH/svhn_cropped-extra.tfrecord*...: 95%|█████████▍| 503453/531131 [00:04<00:00, 155104.53 examples/s]
Shuffling /home/runner/tensorflow_datasets/svhn_cropped/3.0.0.incompleteI408JH/svhn_cropped-extra.tfrecord*...: 98%|█████████▊| 519955/531131 [00:04<00:00, 157926.06 examples/s]
Generating splits...: 100%|██████████| 3/3 [06:32<00:00, 166.49s/ splits]
Dataset svhn_cropped downloaded and prepared to /home/runner/tensorflow_datasets/svhn_cropped/3.0.0. Subsequent calls will reuse this data.
Training on 73257 samples of input shape (32, 32, 3), belonging to 10 classes
We’ll use TensorFlow Dataset to prepare our datasets. We’ll fetch the training dataset as tuples, and the test dataset as numpy arrays
def preprocess(image, label, nclasses=10):
image = tf.cast(image, tf.float32) / 255.0
label = tf.one_hot(tf.squeeze(label), nclasses)
return image, label
batch_size = 1024
train_data = ds_train.map(preprocess, n_classes) # Get dataset as image and one-hot encoded labels, divided by max RGB
train_data = train_data.shuffle(4096).batch(batch_size).prefetch(tf.data.experimental.AUTOTUNE)
for example in train_data.take(1):
break
print("X train batch shape = {}, Y train batch shape = {} ".format(example[0].shape, example[1].shape))
val_data = ds_val.map(preprocess, n_classes)
val_data = val_data.batch(batch_size)
val_data = val_data.prefetch(tf.data.experimental.AUTOTUNE)
# For testing, we get the full dataset in memory as it's rather small.
# We fetch it as numpy arrays to have access to labels and images separately
X_test, Y_test = tfds.as_numpy(tfds.load('svhn_cropped', split='test', batch_size=-1, as_supervised=True))
X_test, Y_test = preprocess(X_test, Y_test, nclasses=n_classes)
print("X test batch shape = {}, Y test batch shape = {} ".format(X_test.shape, Y_test.shape))
X train batch shape = (1024, 32, 32, 3), Y train batch shape = (1024, 10)
2023-06-24 06:06:06.033692: W tensorflow/tsl/framework/cpu_allocator_impl.cc:82] Allocation of 79970304 exceeds 10% of free system memory.
2023-06-24 06:06:06.104545: W tensorflow/tsl/framework/cpu_allocator_impl.cc:82] Allocation of 319881216 exceeds 10% of free system memory.
2023-06-24 06:06:06.359445: W tensorflow/tsl/framework/cpu_allocator_impl.cc:82] Allocation of 319881216 exceeds 10% of free system memory.
X test batch shape = (26032, 32, 32, 3), Y test batch shape = (26032, 10)
Defining the model#
We then need to define a model. For the lowest possible latency, each layer should have a maximum number of trainable parameters of 4096. This is due to fixed limits in the Vivado compiler, beyond which maximally unrolled (=parallel) compilation will fail. This will allow us to use strategy = 'latency' in the hls4ml part, rather than strategy = 'resource', in turn resulting in lower latency
from tensorflow.keras.layers import Input
from tensorflow.keras.layers import BatchNormalization
from tensorflow.keras.layers import Conv2D
from tensorflow.keras.regularizers import l1
from tensorflow.keras.layers import MaxPooling2D
from tensorflow.keras.layers import Activation
from tensorflow.keras.layers import Flatten
from tensorflow.keras.layers import Dense
from tensorflow.keras.models import Model
filters_per_conv_layer = [16, 16, 24]
neurons_per_dense_layer = [42, 64]
x = x_in = Input(input_shape)
for i, f in enumerate(filters_per_conv_layer):
print(('Adding convolutional block {} with N={} filters').format(i, f))
x = Conv2D(
int(f),
kernel_size=(3, 3),
strides=(1, 1),
kernel_initializer='lecun_uniform',
kernel_regularizer=l1(0.0001),
use_bias=False,
name='conv_{}'.format(i),
)(x)
x = BatchNormalization(name='bn_conv_{}'.format(i))(x)
x = Activation('relu', name='conv_act_%i' % i)(x)
x = MaxPooling2D(pool_size=(2, 2), name='pool_{}'.format(i))(x)
x = Flatten()(x)
for i, n in enumerate(neurons_per_dense_layer):
print(('Adding dense block {} with N={} neurons').format(i, n))
x = Dense(n, kernel_initializer='lecun_uniform', kernel_regularizer=l1(0.0001), name='dense_%i' % i, use_bias=False)(x)
x = BatchNormalization(name='bn_dense_{}'.format(i))(x)
x = Activation('relu', name='dense_act_%i' % i)(x)
x = Dense(int(n_classes), name='output_dense')(x)
x_out = Activation('softmax', name='output_softmax')(x)
model = Model(inputs=[x_in], outputs=[x_out], name='keras_baseline')
model.summary()
Adding convolutional block 0 with N=16 filters
Adding convolutional block 1 with N=16 filters
Adding convolutional block 2 with N=24 filters
Adding dense block 0 with N=42 neurons
Adding dense block 1 with N=64 neurons
Model: "keras_baseline"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_1 (InputLayer) [(None, 32, 32, 3)] 0
conv_0 (Conv2D) (None, 30, 30, 16) 432
bn_conv_0 (BatchNormalizati (None, 30, 30, 16) 64
on)
conv_act_0 (Activation) (None, 30, 30, 16) 0
pool_0 (MaxPooling2D) (None, 15, 15, 16) 0
conv_1 (Conv2D) (None, 13, 13, 16) 2304
bn_conv_1 (BatchNormalizati (None, 13, 13, 16) 64
on)
conv_act_1 (Activation) (None, 13, 13, 16) 0
pool_1 (MaxPooling2D) (None, 6, 6, 16) 0
conv_2 (Conv2D) (None, 4, 4, 24) 3456
bn_conv_2 (BatchNormalizati (None, 4, 4, 24) 96
on)
conv_act_2 (Activation) (None, 4, 4, 24) 0
pool_2 (MaxPooling2D) (None, 2, 2, 24) 0
flatten (Flatten) (None, 96) 0
dense_0 (Dense) (None, 42) 4032
bn_dense_0 (BatchNormalizat (None, 42) 168
ion)
dense_act_0 (Activation) (None, 42) 0
dense_1 (Dense) (None, 64) 2688
bn_dense_1 (BatchNormalizat (None, 64) 256
ion)
dense_act_1 (Activation) (None, 64) 0
output_dense (Dense) (None, 10) 650
output_softmax (Activation) (None, 10) 0
=================================================================
Total params: 14,210
Trainable params: 13,886
Non-trainable params: 324
_________________________________________________________________
Lets check if this model can be implemented completely unrolled (=parallel)
for layer in model.layers:
if layer.__class__.__name__ in ['Conv2D', 'Dense']:
w = layer.get_weights()[0]
layersize = np.prod(w.shape)
print("{}: {}".format(layer.name, layersize)) # 0 = weights, 1 = biases
if layersize > 4096: # assuming that shape[0] is batch, i.e., 'None'
print("Layer {} is too large ({}), are you sure you want to train?".format(layer.name, layersize))
conv_0: 432
conv_1: 2304
conv_2: 3456
dense_0: 4032
dense_1: 2688
output_dense: 640
Looks good! It’s below the Vivado-enforced unroll limit of 4096.
Prune dense and convolutional layers#
Since we’ve seen in the previous notebooks that pruning can be done at no accuracy cost, let’s prune the convolutional and dense layers to 50% sparsity, skipping the output layer
import tensorflow_model_optimization as tfmot
from tensorflow_model_optimization.sparsity import keras as sparsity
from tensorflow_model_optimization.python.core.sparsity.keras import pruning_callbacks
NSTEPS = int(train_size * 0.9) // batch_size # 90% train, 10% validation in 10-fold cross validation
print('Number of training steps per epoch is {}'.format(NSTEPS))
# Prune all convolutional and dense layers gradually from 0 to 50% sparsity every 2 epochs,
# ending by the 10th epoch
def pruneFunction(layer):
pruning_params = {
'pruning_schedule': sparsity.PolynomialDecay(
initial_sparsity=0.0, final_sparsity=0.50, begin_step=NSTEPS * 2, end_step=NSTEPS * 10, frequency=NSTEPS
)
}
if isinstance(layer, tf.keras.layers.Conv2D):
return tfmot.sparsity.keras.prune_low_magnitude(layer, **pruning_params)
if isinstance(layer, tf.keras.layers.Dense) and layer.name != 'output_dense':
return tfmot.sparsity.keras.prune_low_magnitude(layer, **pruning_params)
return layer
model_pruned = tf.keras.models.clone_model(model, clone_function=pruneFunction)
Number of training steps per epoch is 64
WARNING:tensorflow:From /usr/share/miniconda/envs/hls4ml-tutorial/lib/python3.10/site-packages/tensorflow/python/autograph/pyct/static_analysis/liveness.py:83: Analyzer.lamba_check (from tensorflow.python.autograph.pyct.static_analysis.liveness) is deprecated and will be removed after 2023-09-23.
Instructions for updating:
Lambda fuctions will be no more assumed to be used in the statement where they are used, or at least in the same block. https://github.com/tensorflow/tensorflow/issues/56089
WARNING:tensorflow:From /usr/share/miniconda/envs/hls4ml-tutorial/lib/python3.10/site-packages/tensorflow/python/autograph/pyct/static_analysis/liveness.py:83: Analyzer.lamba_check (from tensorflow.python.autograph.pyct.static_analysis.liveness) is deprecated and will be removed after 2023-09-23.
Instructions for updating:
Lambda fuctions will be no more assumed to be used in the statement where they are used, or at least in the same block. https://github.com/tensorflow/tensorflow/issues/56089
Train baseline#
We’re now ready to train the model! We defined the batch size and n epochs above. We won’t use callbacks that store the best weights only, since this might select a weight configuration that has not yet reached 50% sparsity.
train = True # True if you want to retrain, false if you want to load a previsously trained model
n_epochs = 30
if train:
LOSS = tf.keras.losses.CategoricalCrossentropy()
OPTIMIZER = tf.keras.optimizers.Adam(learning_rate=3e-3, beta_1=0.9, beta_2=0.999, epsilon=1e-07, amsgrad=True)
model_pruned.compile(loss=LOSS, optimizer=OPTIMIZER, metrics=["accuracy"])
callbacks = [
tf.keras.callbacks.EarlyStopping(patience=10, verbose=1),
tf.keras.callbacks.ReduceLROnPlateau(monitor='val_loss', factor=0.5, patience=3, verbose=1),
pruning_callbacks.UpdatePruningStep(),
]
start = time.time()
model_pruned.fit(train_data, epochs=n_epochs, validation_data=val_data, callbacks=callbacks)
end = time.time()
print('It took {} minutes to train Keras model'.format((end - start) / 60.0))
model_pruned.save('pruned_cnn_model.h5')
else:
from qkeras.utils import _add_supported_quantized_objects
from tensorflow_model_optimization.python.core.sparsity.keras import pruning_wrapper
co = {}
_add_supported_quantized_objects(co)
co['PruneLowMagnitude'] = pruning_wrapper.PruneLowMagnitude
model_pruned = tf.keras.models.load_model('pruned_cnn_model.h5', custom_objects=co)
Epoch 1/30
2023-06-24 06:06:13.505702: W tensorflow/tsl/framework/cpu_allocator_impl.cc:82] Allocation of 58982400 exceeds 10% of free system memory.
2023-06-24 06:06:13.691648: W tensorflow/tsl/framework/cpu_allocator_impl.cc:82] Allocation of 58982400 exceeds 10% of free system memory.
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65/65 [==============================] - 49s 679ms/step - loss: 1.4810 - accuracy: 0.5486 - val_loss: 2.0757 - val_accuracy: 0.2742 - lr: 0.0030
Epoch 2/30
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Epoch 3/30
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Epoch 4/30
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65/65 [==============================] - ETA: 0s - loss: 0.5308 - accuracy: 0.8701
65/65 [==============================] - 44s 665ms/step - loss: 0.5308 - accuracy: 0.8701 - val_loss: 1.2743 - val_accuracy: 0.6158 - lr: 0.0030
Epoch 5/30
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65/65 [==============================] - ETA: 0s - loss: 0.4934 - accuracy: 0.8790
65/65 [==============================] - 45s 685ms/step - loss: 0.4934 - accuracy: 0.8790 - val_loss: 0.8812 - val_accuracy: 0.7438 - lr: 0.0030
Epoch 6/30
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65/65 [==============================] - ETA: 0s - loss: 0.4643 - accuracy: 0.8856
65/65 [==============================] - 44s 666ms/step - loss: 0.4643 - accuracy: 0.8856 - val_loss: 0.7556 - val_accuracy: 0.7921 - lr: 0.0030
Epoch 7/30
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65/65 [==============================] - ETA: 0s - loss: 0.4456 - accuracy: 0.8898
65/65 [==============================] - 43s 659ms/step - loss: 0.4456 - accuracy: 0.8898 - val_loss: 0.6765 - val_accuracy: 0.8197 - lr: 0.0030
Epoch 8/30
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65/65 [==============================] - ETA: 0s - loss: 0.4258 - accuracy: 0.8948
65/65 [==============================] - 43s 660ms/step - loss: 0.4258 - accuracy: 0.8948 - val_loss: 0.5482 - val_accuracy: 0.8620 - lr: 0.0030
Epoch 9/30
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65/65 [==============================] - ETA: 0s - loss: 0.4034 - accuracy: 0.9018
65/65 [==============================] - 44s 663ms/step - loss: 0.4034 - accuracy: 0.9018 - val_loss: 0.5223 - val_accuracy: 0.8665 - lr: 0.0030
Epoch 10/30
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65/65 [==============================] - ETA: 0s - loss: 0.3929 - accuracy: 0.9035
65/65 [==============================] - 45s 678ms/step - loss: 0.3929 - accuracy: 0.9035 - val_loss: 0.4872 - val_accuracy: 0.8793 - lr: 0.0030
Epoch 11/30
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65/65 [==============================] - ETA: 0s - loss: 0.3840 - accuracy: 0.9062
65/65 [==============================] - 44s 675ms/step - loss: 0.3840 - accuracy: 0.9062 - val_loss: 0.4557 - val_accuracy: 0.8840 - lr: 0.0030
Epoch 12/30
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65/65 [==============================] - ETA: 0s - loss: 0.3778 - accuracy: 0.9075
65/65 [==============================] - 45s 679ms/step - loss: 0.3778 - accuracy: 0.9075 - val_loss: 0.4592 - val_accuracy: 0.8838 - lr: 0.0030
Epoch 13/30
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65/65 [==============================] - ETA: 0s - loss: 0.3711 - accuracy: 0.9093
65/65 [==============================] - 45s 680ms/step - loss: 0.3711 - accuracy: 0.9093 - val_loss: 0.4731 - val_accuracy: 0.8807 - lr: 0.0030
Epoch 14/30
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65/65 [==============================] - ETA: 0s - loss: 0.3654 - accuracy: 0.9099
65/65 [==============================] - 44s 672ms/step - loss: 0.3654 - accuracy: 0.9099 - val_loss: 0.4523 - val_accuracy: 0.8868 - lr: 0.0030
Epoch 15/30
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65/65 [==============================] - ETA: 0s - loss: 0.3625 - accuracy: 0.9108
65/65 [==============================] - 44s 661ms/step - loss: 0.3625 - accuracy: 0.9108 - val_loss: 0.4723 - val_accuracy: 0.8793 - lr: 0.0030
Epoch 16/30
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65/65 [==============================] - ETA: 0s - loss: 0.3570 - accuracy: 0.9135
65/65 [==============================] - 44s 663ms/step - loss: 0.3570 - accuracy: 0.9135 - val_loss: 0.4608 - val_accuracy: 0.8832 - lr: 0.0030
Epoch 17/30
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65/65 [==============================] - ETA: 0s - loss: 0.3546 - accuracy: 0.9131
Epoch 17: ReduceLROnPlateau reducing learning rate to 0.001500000013038516.
65/65 [==============================] - 42s 646ms/step - loss: 0.3546 - accuracy: 0.9131 - val_loss: 0.4876 - val_accuracy: 0.8721 - lr: 0.0030
Epoch 18/30
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65/65 [==============================] - ETA: 0s - loss: 0.3356 - accuracy: 0.9195
65/65 [==============================] - 43s 655ms/step - loss: 0.3356 - accuracy: 0.9195 - val_loss: 0.4465 - val_accuracy: 0.8871 - lr: 0.0015
Epoch 19/30
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65/65 [==============================] - ETA: 0s - loss: 0.3282 - accuracy: 0.9212
65/65 [==============================] - 43s 652ms/step - loss: 0.3282 - accuracy: 0.9212 - val_loss: 0.4538 - val_accuracy: 0.8851 - lr: 0.0015
Epoch 20/30
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65/65 [==============================] - ETA: 0s - loss: 0.3268 - accuracy: 0.9216
65/65 [==============================] - 44s 660ms/step - loss: 0.3268 - accuracy: 0.9216 - val_loss: 0.4442 - val_accuracy: 0.8889 - lr: 0.0015
Epoch 21/30
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65/65 [==============================] - ETA: 0s - loss: 0.3253 - accuracy: 0.9219
65/65 [==============================] - 44s 672ms/step - loss: 0.3253 - accuracy: 0.9219 - val_loss: 0.4422 - val_accuracy: 0.8893 - lr: 0.0015
Epoch 22/30
1/65 [..............................] - ETA: 1:15 - loss: 0.2961 - accuracy: 0.9297
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65/65 [==============================] - ETA: 0s - loss: 0.3209 - accuracy: 0.9229
65/65 [==============================] - 43s 661ms/step - loss: 0.3209 - accuracy: 0.9229 - val_loss: 0.4368 - val_accuracy: 0.8913 - lr: 0.0015
Epoch 23/30
1/65 [..............................] - ETA: 1:16 - loss: 0.3875 - accuracy: 0.9102
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65/65 [==============================] - ETA: 0s - loss: 0.3199 - accuracy: 0.9227
65/65 [==============================] - 45s 678ms/step - loss: 0.3199 - accuracy: 0.9227 - val_loss: 0.4451 - val_accuracy: 0.8863 - lr: 0.0015
Epoch 24/30
1/65 [..............................] - ETA: 1:15 - loss: 0.3457 - accuracy: 0.9150
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65/65 [==============================] - ETA: 0s - loss: 0.3187 - accuracy: 0.9229
65/65 [==============================] - 43s 657ms/step - loss: 0.3187 - accuracy: 0.9229 - val_loss: 0.4378 - val_accuracy: 0.8922 - lr: 0.0015
Epoch 25/30
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65/65 [==============================] - ETA: 0s - loss: 0.3168 - accuracy: 0.9237
Epoch 25: ReduceLROnPlateau reducing learning rate to 0.000750000006519258.
65/65 [==============================] - 43s 656ms/step - loss: 0.3168 - accuracy: 0.9237 - val_loss: 0.4425 - val_accuracy: 0.8860 - lr: 0.0015
Epoch 26/30
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65/65 [==============================] - ETA: 0s - loss: 0.3058 - accuracy: 0.9275
65/65 [==============================] - 43s 655ms/step - loss: 0.3058 - accuracy: 0.9275 - val_loss: 0.4269 - val_accuracy: 0.8919 - lr: 7.5000e-04
Epoch 27/30
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65/65 [==============================] - 44s 668ms/step - loss: 0.3039 - accuracy: 0.9282 - val_loss: 0.4281 - val_accuracy: 0.8923 - lr: 7.5000e-04
Epoch 28/30
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65/65 [==============================] - ETA: 0s - loss: 0.3018 - accuracy: 0.9281
65/65 [==============================] - 45s 681ms/step - loss: 0.3018 - accuracy: 0.9281 - val_loss: 0.4365 - val_accuracy: 0.8915 - lr: 7.5000e-04
Epoch 29/30
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Epoch 29: ReduceLROnPlateau reducing learning rate to 0.000375000003259629.
65/65 [==============================] - 44s 669ms/step - loss: 0.3002 - accuracy: 0.9289 - val_loss: 0.4347 - val_accuracy: 0.8908 - lr: 7.5000e-04
Epoch 30/30
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65/65 [==============================] - 44s 663ms/step - loss: 0.2942 - accuracy: 0.9316 - val_loss: 0.4258 - val_accuracy: 0.8933 - lr: 3.7500e-04
It took 32.84859570264816 minutes to train Keras model
You’ll notice the accuracy is lower than that in the hls4ml CNN paper (https://arxiv.org/abs/2101.05108) despite the model being the same. The reson for this is that we didn’t use the extra training data in order to save time. If you want to futher optimize the network, increasing the training data is a good place to start. Enlarging the model architecture comes at a high latency/resource cost.
Quantization and the fused Conv2D+BatchNormalization layer in QKeras#
Let’s now create a pruned an quantized model using QKeras. For this, we will use a fused Convolutional and BatchNormalization (BN) layer from QKeras, which will further speed up the implementation when we implement the model using hls4ml.
There is currently no fused Dense+BatchNoralization layer available in QKeras, so we’ll use Keras BatchNormalization when BN follows a Dense layer for now. We’ll use the same precision everywhere, namely a bit width of 6 and 0 integer bits (this will be implemented as<6,1> in hls4ml, due to the missing sign-bit). For now, make sure to set use_bias=True in QConv2DBatchnorm to avoid problems during synthesis.
from qkeras import QActivation
from qkeras import QDense, QConv2DBatchnorm
x = x_in = Input(shape=input_shape)
for i, f in enumerate(filters_per_conv_layer):
print(('Adding fused QConv+BN block {} with N={} filters').format(i, f))
x = QConv2DBatchnorm(
int(f),
kernel_size=(3, 3),
strides=(1, 1),
kernel_quantizer="quantized_bits(6,0,alpha=1)",
bias_quantizer="quantized_bits(6,0,alpha=1)",
kernel_initializer='lecun_uniform',
kernel_regularizer=l1(0.0001),
use_bias=True,
name='fused_convbn_{}'.format(i),
)(x)
x = QActivation('quantized_relu(6)', name='conv_act_%i' % i)(x)
x = MaxPooling2D(pool_size=(2, 2), name='pool_{}'.format(i))(x)
x = Flatten()(x)
for i, n in enumerate(neurons_per_dense_layer):
print(('Adding QDense block {} with N={} neurons').format(i, n))
x = QDense(
n,
kernel_quantizer="quantized_bits(6,0,alpha=1)",
kernel_initializer='lecun_uniform',
kernel_regularizer=l1(0.0001),
name='dense_%i' % i,
use_bias=False,
)(x)
x = BatchNormalization(name='bn_dense_{}'.format(i))(x)
x = QActivation('quantized_relu(6)', name='dense_act_%i' % i)(x)
x = Dense(int(n_classes), name='output_dense')(x)
x_out = Activation('softmax', name='output_softmax')(x)
qmodel = Model(inputs=[x_in], outputs=[x_out], name='qkeras')
qmodel.summary()
Adding fused QConv+BN block 0 with N=16 filters
Adding fused QConv+BN block 1 with N=16 filters
Adding fused QConv+BN block 2 with N=24 filters
Adding QDense block 0 with N=42 neurons
Adding QDense block 1 with N=64 neurons
Model: "qkeras"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_2 (InputLayer) [(None, 32, 32, 3)] 0
fused_convbn_0 (QConv2DBatc (None, 30, 30, 16) 513
hnorm)
conv_act_0 (QActivation) (None, 30, 30, 16) 0
pool_0 (MaxPooling2D) (None, 15, 15, 16) 0
fused_convbn_1 (QConv2DBatc (None, 13, 13, 16) 2385
hnorm)
conv_act_1 (QActivation) (None, 13, 13, 16) 0
pool_1 (MaxPooling2D) (None, 6, 6, 16) 0
fused_convbn_2 (QConv2DBatc (None, 4, 4, 24) 3577
hnorm)
conv_act_2 (QActivation) (None, 4, 4, 24) 0
pool_2 (MaxPooling2D) (None, 2, 2, 24) 0
flatten_1 (Flatten) (None, 96) 0
dense_0 (QDense) (None, 42) 4032
bn_dense_0 (BatchNormalizat (None, 42) 168
ion)
dense_act_0 (QActivation) (None, 42) 0
dense_1 (QDense) (None, 64) 2688
bn_dense_1 (BatchNormalizat (None, 64) 256
ion)
dense_act_1 (QActivation) (None, 64) 0
output_dense (Dense) (None, 10) 650
output_softmax (Activation) (None, 10) 0
=================================================================
Total params: 14,269
Trainable params: 13,942
Non-trainable params: 327
_________________________________________________________________
# Print the quantized layers
from qkeras.autoqkeras.utils import print_qmodel_summary
print_qmodel_summary(qmodel)
fused_convbn_0 f=16 quantized_bits(6,0,0,alpha=1) quantized_bits(6,0,0,alpha=1)
conv_act_0 quantized_relu(6)
fused_convbn_1 f=16 quantized_bits(6,0,0,alpha=1) quantized_bits(6,0,0,alpha=1)
conv_act_1 quantized_relu(6)
fused_convbn_2 f=24 quantized_bits(6,0,0,alpha=1) quantized_bits(6,0,0,alpha=1)
conv_act_2 quantized_relu(6)
dense_0 u=42 quantized_bits(6,0,0,alpha=1)
bn_dense_0 is normal keras bn layer
dense_act_0 quantized_relu(6)
dense_1 u=64 quantized_bits(6,0,0,alpha=1)
bn_dense_1 is normal keras bn layer
dense_act_1 quantized_relu(6)
You see that a bias quantizer is defined, although we are not using a bias term for the layers. This is set automatically by QKeras. In addition, you’ll note that alpha='1'. This sets the weight scale per channel to 1 (no scaling). The default is alpha='auto_po2', which sets the weight scale per channel to be a power-of-2, such that an actual hardware implementation can be performed by just shifting the result of the convolutional/dense layer to the right or left by checking the sign of the scale and then taking the log2 of the scale.
Let’s now prune and train this model! If you want, you can also train the unpruned version, qmodel and see how the performance compares. We will stick to the pruned one here. Again, we do not use a model checkpoint which stores the best weights, in order to ensure the model is trained to the desired sparsity.
qmodel_pruned = tf.keras.models.clone_model(qmodel, clone_function=pruneFunction)
train = True
n_epochs = 30
if train:
LOSS = tf.keras.losses.CategoricalCrossentropy()
OPTIMIZER = tf.keras.optimizers.Adam(learning_rate=3e-3, beta_1=0.9, beta_2=0.999, epsilon=1e-07, amsgrad=True)
qmodel_pruned.compile(loss=LOSS, optimizer=OPTIMIZER, metrics=["accuracy"])
callbacks = [
tf.keras.callbacks.EarlyStopping(patience=10, verbose=1),
tf.keras.callbacks.ReduceLROnPlateau(monitor='val_loss', factor=0.5, patience=3, verbose=1),
pruning_callbacks.UpdatePruningStep(),
]
start = time.time()
history = qmodel_pruned.fit(train_data, epochs=n_epochs, validation_data=val_data, callbacks=callbacks, verbose=1)
end = time.time()
print('\n It took {} minutes to train!\n'.format((end - start) / 60.0))
qmodel_pruned.save('quantized_pruned_cnn_model.h5')
else:
from qkeras.utils import _add_supported_quantized_objects
from tensorflow_model_optimization.python.core.sparsity.keras import pruning_wrapper
co = {}
_add_supported_quantized_objects(co)
co['PruneLowMagnitude'] = pruning_wrapper.PruneLowMagnitude
qmodel_pruned = tf.keras.models.load_model('quantized_pruned_cnn_model.h5', custom_objects=co)
Epoch 1/30
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65/65 [==============================] - ETA: 0s - loss: 2.1425 - accuracy: 0.2888
65/65 [==============================] - 86s 1s/step - loss: 2.1425 - accuracy: 0.2888 - val_loss: 2.6241 - val_accuracy: 0.1882 - lr: 0.0030
Epoch 2/30
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65/65 [==============================] - ETA: 0s - loss: 1.4019 - accuracy: 0.5799
65/65 [==============================] - 79s 1s/step - loss: 1.4019 - accuracy: 0.5799 - val_loss: 2.1809 - val_accuracy: 0.2862 - lr: 0.0030
Epoch 3/30
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65/65 [==============================] - ETA: 0s - loss: 0.9410 - accuracy: 0.7418
65/65 [==============================] - 79s 1s/step - loss: 0.9410 - accuracy: 0.7418 - val_loss: 1.7295 - val_accuracy: 0.4637 - lr: 0.0030
Epoch 4/30
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65/65 [==============================] - ETA: 0s - loss: 0.7701 - accuracy: 0.7966
65/65 [==============================] - 79s 1s/step - loss: 0.7701 - accuracy: 0.7966 - val_loss: 1.3637 - val_accuracy: 0.5947 - lr: 0.0030
Epoch 5/30
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65/65 [==============================] - ETA: 0s - loss: 0.7357 - accuracy: 0.8039
65/65 [==============================] - 78s 1s/step - loss: 0.7357 - accuracy: 0.8039 - val_loss: 2.2487 - val_accuracy: 0.3007 - lr: 0.0030
Epoch 6/30
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65/65 [==============================] - ETA: 0s - loss: 0.7410 - accuracy: 0.7994
65/65 [==============================] - 78s 1s/step - loss: 0.7410 - accuracy: 0.7994 - val_loss: 1.2550 - val_accuracy: 0.6230 - lr: 0.0030
Epoch 7/30
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65/65 [==============================] - ETA: 0s - loss: 0.6848 - accuracy: 0.8150
65/65 [==============================] - 79s 1s/step - loss: 0.6848 - accuracy: 0.8150 - val_loss: 1.2043 - val_accuracy: 0.6211 - lr: 0.0030
Epoch 8/30
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65/65 [==============================] - 78s 1s/step - loss: 0.6603 - accuracy: 0.8231 - val_loss: 1.0330 - val_accuracy: 0.6923 - lr: 0.0030
Epoch 9/30
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65/65 [==============================] - ETA: 0s - loss: 0.6460 - accuracy: 0.8264
65/65 [==============================] - 78s 1s/step - loss: 0.6460 - accuracy: 0.8264 - val_loss: 0.9844 - val_accuracy: 0.7155 - lr: 0.0030
Epoch 10/30
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65/65 [==============================] - ETA: 0s - loss: 0.5962 - accuracy: 0.8405
65/65 [==============================] - 78s 1s/step - loss: 0.5962 - accuracy: 0.8405 - val_loss: 0.8395 - val_accuracy: 0.7580 - lr: 0.0030
Epoch 11/30
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65/65 [==============================] - ETA: 0s - loss: 0.5552 - accuracy: 0.8546
65/65 [==============================] - 78s 1s/step - loss: 0.5552 - accuracy: 0.8546 - val_loss: 0.7285 - val_accuracy: 0.7969 - lr: 0.0030
Epoch 12/30
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65/65 [==============================] - ETA: 0s - loss: 0.5275 - accuracy: 0.8621
65/65 [==============================] - 78s 1s/step - loss: 0.5275 - accuracy: 0.8621 - val_loss: 0.6416 - val_accuracy: 0.8241 - lr: 0.0030
Epoch 13/30
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65/65 [==============================] - ETA: 0s - loss: 0.5101 - accuracy: 0.8676
65/65 [==============================] - 78s 1s/step - loss: 0.5101 - accuracy: 0.8676 - val_loss: 0.6447 - val_accuracy: 0.8186 - lr: 0.0030
Epoch 14/30
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65/65 [==============================] - ETA: 0s - loss: 0.4951 - accuracy: 0.8718
65/65 [==============================] - 78s 1s/step - loss: 0.4951 - accuracy: 0.8718 - val_loss: 0.6431 - val_accuracy: 0.8238 - lr: 0.0030
Epoch 15/30
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65/65 [==============================] - ETA: 0s - loss: 0.4901 - accuracy: 0.8737
65/65 [==============================] - 78s 1s/step - loss: 0.4901 - accuracy: 0.8737 - val_loss: 0.5724 - val_accuracy: 0.8490 - lr: 0.0030
Epoch 16/30
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65/65 [==============================] - ETA: 0s - loss: 0.4757 - accuracy: 0.8775
65/65 [==============================] - 75s 1s/step - loss: 0.4757 - accuracy: 0.8775 - val_loss: 0.5841 - val_accuracy: 0.8421 - lr: 0.0030
Epoch 17/30
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65/65 [==============================] - ETA: 0s - loss: 0.4690 - accuracy: 0.8790
65/65 [==============================] - 78s 1s/step - loss: 0.4690 - accuracy: 0.8790 - val_loss: 0.6280 - val_accuracy: 0.8294 - lr: 0.0030
Epoch 18/30
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65/65 [==============================] - ETA: 0s - loss: 0.4677 - accuracy: 0.8793
65/65 [==============================] - 78s 1s/step - loss: 0.4677 - accuracy: 0.8793 - val_loss: 0.5652 - val_accuracy: 0.8490 - lr: 0.0030
Epoch 19/30
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65/65 [==============================] - ETA: 0s - loss: 0.4833 - accuracy: 0.8732
65/65 [==============================] - 75s 1s/step - loss: 0.4833 - accuracy: 0.8732 - val_loss: 0.7899 - val_accuracy: 0.7772 - lr: 0.0030
Epoch 20/30
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65/65 [==============================] - ETA: 0s - loss: 0.4842 - accuracy: 0.8734
65/65 [==============================] - 78s 1s/step - loss: 0.4842 - accuracy: 0.8734 - val_loss: 0.7937 - val_accuracy: 0.7733 - lr: 0.0030
Epoch 21/30
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65/65 [==============================] - ETA: 0s - loss: 0.4648 - accuracy: 0.8796
Epoch 21: ReduceLROnPlateau reducing learning rate to 0.001500000013038516.
65/65 [==============================] - 82s 1s/step - loss: 0.4648 - accuracy: 0.8796 - val_loss: 0.6558 - val_accuracy: 0.8172 - lr: 0.0030
Epoch 22/30
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65/65 [==============================] - ETA: 0s - loss: 0.4448 - accuracy: 0.8863
65/65 [==============================] - 78s 1s/step - loss: 0.4448 - accuracy: 0.8863 - val_loss: 0.6048 - val_accuracy: 0.8317 - lr: 0.0015
Epoch 23/30
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65/65 [==============================] - ETA: 0s - loss: 0.4408 - accuracy: 0.8866
65/65 [==============================] - 80s 1s/step - loss: 0.4408 - accuracy: 0.8866 - val_loss: 0.5602 - val_accuracy: 0.8468 - lr: 0.0015
Epoch 24/30
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65/65 [==============================] - ETA: 0s - loss: 0.4313 - accuracy: 0.8903
65/65 [==============================] - 77s 1s/step - loss: 0.4313 - accuracy: 0.8903 - val_loss: 0.5785 - val_accuracy: 0.8415 - lr: 0.0015
Epoch 25/30
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65/65 [==============================] - ETA: 0s - loss: 0.4253 - accuracy: 0.8915
65/65 [==============================] - 78s 1s/step - loss: 0.4253 - accuracy: 0.8915 - val_loss: 0.5386 - val_accuracy: 0.8567 - lr: 0.0015
Epoch 26/30
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65/65 [==============================] - ETA: 0s - loss: 0.4225 - accuracy: 0.8923
65/65 [==============================] - 76s 1s/step - loss: 0.4225 - accuracy: 0.8923 - val_loss: 0.5437 - val_accuracy: 0.8544 - lr: 0.0015
Epoch 27/30
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65/65 [==============================] - ETA: 0s - loss: 0.4181 - accuracy: 0.8943
65/65 [==============================] - 78s 1s/step - loss: 0.4181 - accuracy: 0.8943 - val_loss: 0.5377 - val_accuracy: 0.8549 - lr: 0.0015
Epoch 28/30
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65/65 [==============================] - 80s 1s/step - loss: 0.4169 - accuracy: 0.8939 - val_loss: 0.5285 - val_accuracy: 0.8580 - lr: 0.0015
Epoch 29/30
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65/65 [==============================] - 78s 1s/step - loss: 0.4142 - accuracy: 0.8938 - val_loss: 0.5302 - val_accuracy: 0.8609 - lr: 0.0015
Epoch 30/30
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65/65 [==============================] - ETA: 0s - loss: 0.4105 - accuracy: 0.8959
65/65 [==============================] - 78s 1s/step - loss: 0.4105 - accuracy: 0.8959 - val_loss: 0.5353 - val_accuracy: 0.8553 - lr: 0.0015
It took 40.420630697409315 minutes to train!
We note that training a model quantization aware, takes around twice as long as when not quantizing during training! The validation accuracy is very similar to that of the floating point model equivalent, despite containing significantly less information
Performance#
Let’s look at some ROC curves to compare the performance. Lets choose a few numbers so it doesn’t get confusing. Feel free to change the numbers in labels.
predict_baseline = model_pruned.predict(X_test)
test_score_baseline = model_pruned.evaluate(X_test, Y_test)
predict_qkeras = qmodel_pruned.predict(X_test)
test_score_qkeras = qmodel_pruned.evaluate(X_test, Y_test)
print('Keras accuracy = {} , QKeras 6-bit accuracy = {}'.format(test_score_baseline[1], test_score_qkeras[1]))
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814/814 [==============================] - ETA: 0s - loss: 0.5639 - accuracy: 0.8500
814/814 [==============================] - 8s 10ms/step - loss: 0.5639 - accuracy: 0.8500
Keras accuracy = 0.8884834051132202 , QKeras 6-bit accuracy = 0.8499538898468018
import matplotlib.pyplot as plt
import pandas as pd
from sklearn import metrics
labels = ['%i' % nr for nr in range(0, n_classes)] # If you want to look at all the labels
# labels = ['0','1','9'] # Look at only a few labels, here for digits 0, 1 and 9
print('Plotting ROC for labels {}'.format(labels))
df = pd.DataFrame()
df_q = pd.DataFrame()
fpr = {}
tpr = {}
auc1 = {}
fpr_q = {}
tpr_q = {}
auc1_q = {}
%matplotlib inline
colors = ['#67001f', '#b2182b', '#d6604d', '#f4a582', '#fddbc7', '#d1e5f0', '#92c5de', '#4393c3', '#2166ac', '#053061']
fig, ax = plt.subplots(figsize=(10, 10))
for i, label in enumerate(labels):
df[label] = Y_test[:, int(label)]
df[label + '_pred'] = predict_baseline[:, int(label)]
fpr[label], tpr[label], threshold = metrics.roc_curve(df[label], df[label + '_pred'])
auc1[label] = metrics.auc(fpr[label], tpr[label])
df_q[label] = Y_test[:, int(label)]
df_q[label + '_pred'] = predict_qkeras[:, int(label)]
fpr_q[label], tpr_q[label], threshold_q = metrics.roc_curve(df_q[label], df_q[label + '_pred'])
auc1_q[label] = metrics.auc(fpr_q[label], tpr_q[label])
plt.plot(
fpr[label],
tpr[label],
label=r'{}, AUC Keras = {:.1f}% AUC QKeras = {:.1f}%)'.format(label, auc1[label] * 100, auc1_q[label] * 100),
linewidth=1.5,
c=colors[i],
linestyle='solid',
)
plt.plot(fpr_q[label], tpr_q[label], linewidth=1.5, c=colors[i], linestyle='dotted')
plt.semilogx()
plt.ylabel("True Positive Rate")
plt.xlabel("False Positive Rate")
plt.xlim(0.01, 1.0)
plt.ylim(0.5, 1.1)
plt.legend(loc='lower right')
plt.figtext(
0.2,
0.83,
r'Accuracy Keras = {:.1f}% QKeras 8-bit = {:.1f}%'.format(test_score_baseline[1] * 100, test_score_qkeras[1] * 100),
wrap=True,
horizontalalignment='left',
verticalalignment='center',
)
from matplotlib.lines import Line2D
lines = [Line2D([0], [0], ls='-'), Line2D([0], [0], ls='--')]
from matplotlib.legend import Legend
leg = Legend(ax, lines, labels=['Keras', 'QKeras'], loc='lower right', frameon=False)
ax.add_artist(leg)
Plotting ROC for labels ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']
<matplotlib.legend.Legend at 0x7f5e59132c80>
The difference in AUC between the fp32 Keras model and the 8-bit QKeras model, is small, as we have seen for the previous examples. You can find a bonus exercise below, Bonus: Automatic quantization, where we’ll use AutoQKeras to find the best heterogeneously quantized model, given a set of resource and accuracy constriants.
Check sparsity#
Let’s also check the per-layer sparsity:
def doWeights(model):
allWeightsByLayer = {}
for layer in model.layers:
if (layer._name).find("batch") != -1 or len(layer.get_weights()) < 1:
continue
weights = layer.weights[0].numpy().flatten()
allWeightsByLayer[layer._name] = weights
print('Layer {}: % of zeros = {}'.format(layer._name, np.sum(weights == 0) / np.size(weights)))
labelsW = []
histosW = []
for key in reversed(sorted(allWeightsByLayer.keys())):
labelsW.append(key)
histosW.append(allWeightsByLayer[key])
fig = plt.figure(figsize=(10, 10))
bins = np.linspace(-1.5, 1.5, 50)
plt.hist(histosW, bins, histtype='stepfilled', stacked=True, label=labelsW, edgecolor='black')
plt.legend(frameon=False, loc='upper left')
plt.ylabel('Number of Weights')
plt.xlabel('Weights')
plt.figtext(0.2, 0.38, model._name, wrap=True, horizontalalignment='left', verticalalignment='center')
doWeights(model_pruned)
doWeights(qmodel_pruned)
Layer prune_low_magnitude_conv_0: % of zeros = 0.5
Layer bn_conv_0: % of zeros = 0.0
Layer prune_low_magnitude_conv_1: % of zeros = 0.5
Layer bn_conv_1: % of zeros = 0.0
Layer prune_low_magnitude_conv_2: % of zeros = 0.5
Layer bn_conv_2: % of zeros = 0.0
Layer prune_low_magnitude_dense_0: % of zeros = 0.5
Layer bn_dense_0: % of zeros = 0.0
Layer prune_low_magnitude_dense_1: % of zeros = 0.5
Layer bn_dense_1: % of zeros = 0.0
Layer output_dense: % of zeros = 0.0
Layer prune_low_magnitude_fused_convbn_0: % of zeros = 0.5
Layer prune_low_magnitude_fused_convbn_1: % of zeros = 0.5
Layer prune_low_magnitude_fused_convbn_2: % of zeros = 0.5
Layer prune_low_magnitude_dense_0: % of zeros = 0.5
Layer bn_dense_0: % of zeros = 0.0
Layer prune_low_magnitude_dense_1: % of zeros = 0.5
Layer bn_dense_1: % of zeros = 0.0
Layer output_dense: % of zeros = 0.0
We see that 50% of the weights per layer are set to zero, as expected. Now, let’s synthesize the floating point Keras model and the QKeras quantized model!
CNNs in hls4ml#
In this part, we will take the two models we trained above (the floating-point 32 Keras model and the 6-bit QKeras model), and synthesize them with hls4ml. Although your models are probably already in memory, let’s load them from scratch. We need to pass the appropriate custom QKeras/pruning layers when loading, and remove the pruning parameters that were saved together with the model.
from tensorflow_model_optimization.sparsity.keras import strip_pruning
from tensorflow_model_optimization.python.core.sparsity.keras import pruning_wrapper
from qkeras.utils import _add_supported_quantized_objects
co = {}
_add_supported_quantized_objects(co)
co['PruneLowMagnitude'] = pruning_wrapper.PruneLowMagnitude
model = tf.keras.models.load_model('pruned_cnn_model.h5', custom_objects=co)
model = strip_pruning(model)
qmodel = tf.keras.models.load_model('quantized_pruned_cnn_model.h5', custom_objects=co)
qmodel = strip_pruning(qmodel)
Now, we need to define the hls4ml and Vivado configurations. Two things will change with respect to what was done in the previous exercises. First, we will use IOType= 'io_stream' in the Vivado configuration.
You must use IOType= 'io_stream' if attempting to synthesize a large convolutional neural network.
The CNN implementation in hls4ml is based on streams, which are synthesized in hardware as first in, first out (FIFO) buffers. Shift registers are used to keep track of the last <kernel height - 1> rows of input pixels, and maintains a shifting snapshot of the convolution kernel.
This is illustrated in the gif below. Here, the input image is at the top-left and the output image at the bottom left. The top right image shows the internal state of the shift registers and convolutional kernel. The red square indicates the current pixels contained within the convolutional kernel.
Lastly, we will use ['Strategy'] = 'Latency' for all the layers in the hls4ml configuration. If one layer would have >4096 elements, we sould set ['Strategy'] = 'Resource' for that layer, or increase the reuse factor by hand. You can find examples of how to do this below.
import hls4ml
import plotting
# First, the baseline model
hls_config = hls4ml.utils.config_from_keras_model(model, granularity='name')
# Set the precision and reuse factor for the full model
hls_config['Model']['Precision'] = 'ap_fixed<16,6>'
hls_config['Model']['ReuseFactor'] = 1
# Create an entry for each layer, here you can for instance change the strategy for a layer to 'resource'
# or increase the reuse factor individually for large layers.
# In this case, we designed the model to be small enough for a fully parallel implementation
# so we use the latency strategy and reuse factor of 1 for all layers.
for Layer in hls_config['LayerName'].keys():
hls_config['LayerName'][Layer]['Strategy'] = 'Latency'
hls_config['LayerName'][Layer]['ReuseFactor'] = 1
# If you want best numerical performance for high-accuray models, while the default latency strategy is faster but numerically more unstable
hls_config['LayerName']['output_softmax']['Strategy'] = 'Stable'
plotting.print_dict(hls_config)
cfg = hls4ml.converters.create_config(backend='Vivado')
cfg['IOType'] = 'io_stream' # Must set this if using CNNs!
cfg['HLSConfig'] = hls_config
cfg['KerasModel'] = model
cfg['OutputDir'] = 'pruned_cnn/'
cfg['XilinxPart'] = 'xcu250-figd2104-2L-e'
hls_model = hls4ml.converters.keras_to_hls(cfg)
hls_model.compile()
WARNING: Failed to import handlers from convolution.py: No module named 'torch'.
WARNING: Failed to import handlers from core.py: No module named 'torch'.
Interpreting Model
Topology:
Layer name: input_1, layer type: InputLayer, input shapes: [[None, 32, 32, 3]], output shape: [None, 32, 32, 3]
Layer name: conv_0, layer type: Conv2D, input shapes: [[None, 32, 32, 3]], output shape: [None, 30, 30, 16]
Layer name: bn_conv_0, layer type: BatchNormalization, input shapes: [[None, 30, 30, 16]], output shape: [None, 30, 30, 16]
Layer name: conv_act_0, layer type: Activation, input shapes: [[None, 30, 30, 16]], output shape: [None, 30, 30, 16]
Layer name: pool_0, layer type: MaxPooling2D, input shapes: [[None, 30, 30, 16]], output shape: [None, 15, 15, 16]
Layer name: conv_1, layer type: Conv2D, input shapes: [[None, 15, 15, 16]], output shape: [None, 13, 13, 16]
Layer name: bn_conv_1, layer type: BatchNormalization, input shapes: [[None, 13, 13, 16]], output shape: [None, 13, 13, 16]
Layer name: conv_act_1, layer type: Activation, input shapes: [[None, 13, 13, 16]], output shape: [None, 13, 13, 16]
Layer name: pool_1, layer type: MaxPooling2D, input shapes: [[None, 13, 13, 16]], output shape: [None, 6, 6, 16]
Layer name: conv_2, layer type: Conv2D, input shapes: [[None, 6, 6, 16]], output shape: [None, 4, 4, 24]
Layer name: bn_conv_2, layer type: BatchNormalization, input shapes: [[None, 4, 4, 24]], output shape: [None, 4, 4, 24]
Layer name: conv_act_2, layer type: Activation, input shapes: [[None, 4, 4, 24]], output shape: [None, 4, 4, 24]
Layer name: pool_2, layer type: MaxPooling2D, input shapes: [[None, 4, 4, 24]], output shape: [None, 2, 2, 24]
Layer name: flatten, layer type: Reshape, input shapes: [[None, 2, 2, 24]], output shape: [None, 96]
Layer name: dense_0, layer type: Dense, input shapes: [[None, 96]], output shape: [None, 42]
Layer name: bn_dense_0, layer type: BatchNormalization, input shapes: [[None, 42]], output shape: [None, 42]
Layer name: dense_act_0, layer type: Activation, input shapes: [[None, 42]], output shape: [None, 42]
Layer name: dense_1, layer type: Dense, input shapes: [[None, 42]], output shape: [None, 64]
Layer name: bn_dense_1, layer type: BatchNormalization, input shapes: [[None, 64]], output shape: [None, 64]
Layer name: dense_act_1, layer type: Activation, input shapes: [[None, 64]], output shape: [None, 64]
Layer name: output_dense, layer type: Dense, input shapes: [[None, 64]], output shape: [None, 10]
Layer name: output_softmax, layer type: Softmax, input shapes: [[None, 10]], output shape: [None, 10]
Model
Precision: ap_fixed<16,6>
ReuseFactor: 1
Strategy: Latency
BramFactor: 1000000000
TraceOutput: False
LayerName
input_1
Trace: False
Precision
result: fixed<16,6>
Strategy: Latency
ReuseFactor: 1
conv_0
Trace: False
Precision
result: fixed<16,6>
weight: fixed<16,6>
bias: fixed<16,6>
Strategy: Latency
ReuseFactor: 1
conv_0_linear
Trace: False
Precision
result: fixed<16,6>
Strategy: Latency
ReuseFactor: 1
bn_conv_0
Trace: False
Precision
result: fixed<16,6>
scale: fixed<16,6>
bias: fixed<16,6>
Strategy: Latency
ReuseFactor: 1
conv_act_0
Trace: False
Precision
result: fixed<16,6>
Strategy: Latency
ReuseFactor: 1
pool_0
Trace: False
Precision
result: fixed<16,6>
Strategy: Latency
ReuseFactor: 1
conv_1
Trace: False
Precision
result: fixed<16,6>
weight: fixed<16,6>
bias: fixed<16,6>
Strategy: Latency
ReuseFactor: 1
conv_1_linear
Trace: False
Precision
result: fixed<16,6>
Strategy: Latency
ReuseFactor: 1
bn_conv_1
Trace: False
Precision
result: fixed<16,6>
scale: fixed<16,6>
bias: fixed<16,6>
Strategy: Latency
ReuseFactor: 1
conv_act_1
Trace: False
Precision
result: fixed<16,6>
Strategy: Latency
ReuseFactor: 1
pool_1
Trace: False
Precision
result: fixed<16,6>
Strategy: Latency
ReuseFactor: 1
conv_2
Trace: False
Precision
result: fixed<16,6>
weight: fixed<16,6>
bias: fixed<16,6>
Strategy: Latency
ReuseFactor: 1
conv_2_linear
Trace: False
Precision
result: fixed<16,6>
Strategy: Latency
ReuseFactor: 1
bn_conv_2
Trace: False
Precision
result: fixed<16,6>
scale: fixed<16,6>
bias: fixed<16,6>
Strategy: Latency
ReuseFactor: 1
conv_act_2
Trace: False
Precision
result: fixed<16,6>
Strategy: Latency
ReuseFactor: 1
pool_2
Trace: False
Precision
result: fixed<16,6>
Strategy: Latency
ReuseFactor: 1
flatten
Trace: False
Precision
result: fixed<16,6>
Strategy: Latency
ReuseFactor: 1
dense_0
Trace: False
Precision
result: fixed<16,6>
weight: fixed<16,6>
bias: fixed<16,6>
Strategy: Latency
ReuseFactor: 1
dense_0_linear
Trace: False
Precision
result: fixed<16,6>
Strategy: Latency
ReuseFactor: 1
bn_dense_0
Trace: False
Precision
result: fixed<16,6>
scale: fixed<16,6>
bias: fixed<16,6>
Strategy: Latency
ReuseFactor: 1
dense_act_0
Trace: False
Precision
result: fixed<16,6>
Strategy: Latency
ReuseFactor: 1
dense_1
Trace: False
Precision
result: fixed<16,6>
weight: fixed<16,6>
bias: fixed<16,6>
Strategy: Latency
ReuseFactor: 1
dense_1_linear
Trace: False
Precision
result: fixed<16,6>
Strategy: Latency
ReuseFactor: 1
bn_dense_1
Trace: False
Precision
result: fixed<16,6>
scale: fixed<16,6>
bias: fixed<16,6>
Strategy: Latency
ReuseFactor: 1
dense_act_1
Trace: False
Precision
result: fixed<16,6>
Strategy: Latency
ReuseFactor: 1
output_dense
Trace: False
Precision
result: fixed<16,6>
weight: fixed<16,6>
bias: fixed<16,6>
Strategy: Latency
ReuseFactor: 1
output_dense_linear
Trace: False
Precision
result: fixed<16,6>
Strategy: Latency
ReuseFactor: 1
output_softmax
Trace: False
Precision
result: fixed<16,6>
Strategy: Stable
ReuseFactor: 1
Interpreting Model
Topology:
Layer name: input_1, layer type: InputLayer, input shapes: [[None, 32, 32, 3]], output shape: [None, 32, 32, 3]
Layer name: conv_0, layer type: Conv2D, input shapes: [[None, 32, 32, 3]], output shape: [None, 30, 30, 16]
Layer name: bn_conv_0, layer type: BatchNormalization, input shapes: [[None, 30, 30, 16]], output shape: [None, 30, 30, 16]
Layer name: conv_act_0, layer type: Activation, input shapes: [[None, 30, 30, 16]], output shape: [None, 30, 30, 16]
Layer name: pool_0, layer type: MaxPooling2D, input shapes: [[None, 30, 30, 16]], output shape: [None, 15, 15, 16]
Layer name: conv_1, layer type: Conv2D, input shapes: [[None, 15, 15, 16]], output shape: [None, 13, 13, 16]
Layer name: bn_conv_1, layer type: BatchNormalization, input shapes: [[None, 13, 13, 16]], output shape: [None, 13, 13, 16]
Layer name: conv_act_1, layer type: Activation, input shapes: [[None, 13, 13, 16]], output shape: [None, 13, 13, 16]
Layer name: pool_1, layer type: MaxPooling2D, input shapes: [[None, 13, 13, 16]], output shape: [None, 6, 6, 16]
Layer name: conv_2, layer type: Conv2D, input shapes: [[None, 6, 6, 16]], output shape: [None, 4, 4, 24]
Layer name: bn_conv_2, layer type: BatchNormalization, input shapes: [[None, 4, 4, 24]], output shape: [None, 4, 4, 24]
Layer name: conv_act_2, layer type: Activation, input shapes: [[None, 4, 4, 24]], output shape: [None, 4, 4, 24]
Layer name: pool_2, layer type: MaxPooling2D, input shapes: [[None, 4, 4, 24]], output shape: [None, 2, 2, 24]
Layer name: flatten, layer type: Reshape, input shapes: [[None, 2, 2, 24]], output shape: [None, 96]
Layer name: dense_0, layer type: Dense, input shapes: [[None, 96]], output shape: [None, 42]
Layer name: bn_dense_0, layer type: BatchNormalization, input shapes: [[None, 42]], output shape: [None, 42]
Layer name: dense_act_0, layer type: Activation, input shapes: [[None, 42]], output shape: [None, 42]
Layer name: dense_1, layer type: Dense, input shapes: [[None, 42]], output shape: [None, 64]
Layer name: bn_dense_1, layer type: BatchNormalization, input shapes: [[None, 64]], output shape: [None, 64]
Layer name: dense_act_1, layer type: Activation, input shapes: [[None, 64]], output shape: [None, 64]
Layer name: output_dense, layer type: Dense, input shapes: [[None, 64]], output shape: [None, 10]
Layer name: output_softmax, layer type: Softmax, input shapes: [[None, 10]], output shape: [None, 10]
Creating HLS model
WARNING: Cannot use "Latency" model strategy for conv_0 layer. Switching to "Resource" strategy.
Writing HLS project
WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.
/usr/share/miniconda/envs/hls4ml-tutorial/lib/python3.10/site-packages/hls4ml/converters/__init__.py:24: UserWarning: WARNING: Pytorch converter is not enabled!
warnings.warn("WARNING: Pytorch converter is not enabled!", stacklevel=1)
WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.
Done
Let’s get a nice overview over the various shapes and precisions used for each layer through hls4ml.utils.plot_model, as well as look at the weight profile using hls4ml.model.profiling.numerical. The weight profiling returns two plots: Before (top) and after (bottom) various optimizations applied to the HLS model before the final translation to HLS, for instance the fusing of Dense and BatchNormalization layers.
hls4ml.utils.plot_model(hls_model, show_shapes=True, show_precision=True, to_file=None)
hls4ml.model.profiling.numerical(model=model, hls_model=hls_model)
Interpreting Model
Topology:
Layer name: input_1, layer type: InputLayer, input shapes: [[None, 32, 32, 3]], output shape: [None, 32, 32, 3]
Layer name: conv_0, layer type: Conv2D, input shapes: [[None, 32, 32, 3]], output shape: [None, 30, 30, 16]
Layer name: bn_conv_0, layer type: BatchNormalization, input shapes: [[None, 30, 30, 16]], output shape: [None, 30, 30, 16]
Layer name: conv_act_0, layer type: Activation, input shapes: [[None, 30, 30, 16]], output shape: [None, 30, 30, 16]
Layer name: pool_0, layer type: MaxPooling2D, input shapes: [[None, 30, 30, 16]], output shape: [None, 15, 15, 16]
Layer name: conv_1, layer type: Conv2D, input shapes: [[None, 15, 15, 16]], output shape: [None, 13, 13, 16]
Layer name: bn_conv_1, layer type: BatchNormalization, input shapes: [[None, 13, 13, 16]], output shape: [None, 13, 13, 16]
Layer name: conv_act_1, layer type: Activation, input shapes: [[None, 13, 13, 16]], output shape: [None, 13, 13, 16]
Layer name: pool_1, layer type: MaxPooling2D, input shapes: [[None, 13, 13, 16]], output shape: [None, 6, 6, 16]
Layer name: conv_2, layer type: Conv2D, input shapes: [[None, 6, 6, 16]], output shape: [None, 4, 4, 24]
Layer name: bn_conv_2, layer type: BatchNormalization, input shapes: [[None, 4, 4, 24]], output shape: [None, 4, 4, 24]
Layer name: conv_act_2, layer type: Activation, input shapes: [[None, 4, 4, 24]], output shape: [None, 4, 4, 24]
Layer name: pool_2, layer type: MaxPooling2D, input shapes: [[None, 4, 4, 24]], output shape: [None, 2, 2, 24]
Layer name: flatten, layer type: Reshape, input shapes: [[None, 2, 2, 24]], output shape: [None, 96]
Layer name: dense_0, layer type: Dense, input shapes: [[None, 96]], output shape: [None, 42]
Layer name: bn_dense_0, layer type: BatchNormalization, input shapes: [[None, 42]], output shape: [None, 42]
Layer name: dense_act_0, layer type: Activation, input shapes: [[None, 42]], output shape: [None, 42]
Layer name: dense_1, layer type: Dense, input shapes: [[None, 42]], output shape: [None, 64]
Layer name: bn_dense_1, layer type: BatchNormalization, input shapes: [[None, 64]], output shape: [None, 64]
Layer name: dense_act_1, layer type: Activation, input shapes: [[None, 64]], output shape: [None, 64]
Layer name: output_dense, layer type: Dense, input shapes: [[None, 64]], output shape: [None, 10]
Layer name: output_softmax, layer type: Softmax, input shapes: [[None, 10]], output shape: [None, 10]
Creating HLS model
WARNING: Cannot use "Latency" model strategy for conv_0 layer. Switching to "Resource" strategy.
Profiling weights (before optimization)
Profiling weights (final / after optimization)
(<Figure size 640x480 with 1 Axes>,
<Figure size 640x480 with 1 Axes>,
None,
None)
The colored boxes are the distribution of the weights of the model, and the gray band illustrates the numerical range covered by the chosen fixed point precision. As we configured, this model uses a precision of ap_fixed<16,6> for all layers of the model. Let’s now build our QKeras model
# Then the QKeras model
hls_config_q = hls4ml.utils.config_from_keras_model(qmodel, granularity='name')
hls_config_q['Model']['ReuseFactor'] = 1
hls_config['Model']['Precision'] = 'ap_fixed<16,6>'
hls_config_q['LayerName']['output_softmax']['Strategy'] = 'Stable'
plotting.print_dict(hls_config_q)
cfg_q = hls4ml.converters.create_config(backend='Vivado')
cfg_q['IOType'] = 'io_stream' # Must set this if using CNNs!
cfg_q['HLSConfig'] = hls_config_q
cfg_q['KerasModel'] = qmodel
cfg_q['OutputDir'] = 'quantized_pruned_cnn/'
cfg_q['XilinxPart'] = 'xcu250-figd2104-2L-e'
hls_model_q = hls4ml.converters.keras_to_hls(cfg_q)
hls_model_q.compile()
Interpreting Model
Topology:
Layer name: input_2, layer type: InputLayer, input shapes: [[None, 32, 32, 3]], output shape: [None, 32, 32, 3]
Layer name: fused_convbn_0, layer type: QConv2DBatchnorm, input shapes: [[None, 32, 32, 3]], output shape: [None, 30, 30, 16]
Layer name: conv_act_0, layer type: Activation, input shapes: [[None, 30, 30, 16]], output shape: [None, 30, 30, 16]
Layer name: pool_0, layer type: MaxPooling2D, input shapes: [[None, 30, 30, 16]], output shape: [None, 15, 15, 16]
Layer name: fused_convbn_1, layer type: QConv2DBatchnorm, input shapes: [[None, 15, 15, 16]], output shape: [None, 13, 13, 16]
Layer name: conv_act_1, layer type: Activation, input shapes: [[None, 13, 13, 16]], output shape: [None, 13, 13, 16]
Layer name: pool_1, layer type: MaxPooling2D, input shapes: [[None, 13, 13, 16]], output shape: [None, 6, 6, 16]
Layer name: fused_convbn_2, layer type: QConv2DBatchnorm, input shapes: [[None, 6, 6, 16]], output shape: [None, 4, 4, 24]
Layer name: conv_act_2, layer type: Activation, input shapes: [[None, 4, 4, 24]], output shape: [None, 4, 4, 24]
Layer name: pool_2, layer type: MaxPooling2D, input shapes: [[None, 4, 4, 24]], output shape: [None, 2, 2, 24]
Layer name: flatten_1, layer type: Reshape, input shapes: [[None, 2, 2, 24]], output shape: [None, 96]
Layer name: dense_0, layer type: QDense, input shapes: [[None, 96]], output shape: [None, 42]
Layer name: bn_dense_0, layer type: BatchNormalization, input shapes: [[None, 42]], output shape: [None, 42]
Layer name: dense_act_0, layer type: Activation, input shapes: [[None, 42]], output shape: [None, 42]
Layer name: dense_1, layer type: QDense, input shapes: [[None, 42]], output shape: [None, 64]
Layer name: bn_dense_1, layer type: BatchNormalization, input shapes: [[None, 64]], output shape: [None, 64]
Layer name: dense_act_1, layer type: Activation, input shapes: [[None, 64]], output shape: [None, 64]
Layer name: output_dense, layer type: Dense, input shapes: [[None, 64]], output shape: [None, 10]
Layer name: output_softmax, layer type: Softmax, input shapes: [[None, 10]], output shape: [None, 10]
Model
Precision: fixed<16,6>
ReuseFactor: 1
Strategy: Latency
BramFactor: 1000000000
TraceOutput: False
LayerName
input_2
Trace: False
Precision
result: fixed<16,6>
fused_convbn_0
Trace: False
Precision
result: fixed<16,6>
weight: fixed<6,1>
bias: fixed<6,1>
fused_convbn_0_linear
Trace: False
Precision
result: fixed<16,6>
conv_act_0
Trace: False
Precision
result: ufixed<6,0,RND_CONV,SAT>
pool_0
Trace: False
Precision
result: fixed<16,6>
fused_convbn_1
Trace: False
Precision
result: fixed<16,6>
weight: fixed<6,1>
bias: fixed<6,1>
fused_convbn_1_linear
Trace: False
Precision
result: fixed<16,6>
conv_act_1
Trace: False
Precision
result: ufixed<6,0,RND_CONV,SAT>
pool_1
Trace: False
Precision
result: fixed<16,6>
fused_convbn_2
Trace: False
Precision
result: fixed<16,6>
weight: fixed<6,1>
bias: fixed<6,1>
fused_convbn_2_linear
Trace: False
Precision
result: fixed<16,6>
conv_act_2
Trace: False
Precision
result: ufixed<6,0,RND_CONV,SAT>
pool_2
Trace: False
Precision
result: fixed<16,6>
flatten_1
Trace: False
Precision
result: fixed<16,6>
dense_0
Trace: False
Precision
result: fixed<16,6>
weight: fixed<6,1>
bias: fixed<16,6>
dense_0_linear
Trace: False
Precision
result: fixed<16,6>
bn_dense_0
Trace: False
Precision
result: fixed<16,6>
scale: fixed<16,6>
bias: fixed<16,6>
dense_act_0
Trace: False
Precision
result: ufixed<6,0,RND_CONV,SAT>
dense_1
Trace: False
Precision
result: fixed<16,6>
weight: fixed<6,1>
bias: fixed<16,6>
dense_1_linear
Trace: False
Precision
result: fixed<16,6>
bn_dense_1
Trace: False
Precision
result: fixed<16,6>
scale: fixed<16,6>
bias: fixed<16,6>
dense_act_1
Trace: False
Precision
result: ufixed<6,0,RND_CONV,SAT>
output_dense
Trace: False
Precision
result: fixed<16,6>
weight: fixed<16,6>
bias: fixed<16,6>
output_dense_linear
Trace: False
Precision
result: fixed<16,6>
output_softmax
Trace: False
Precision
result: fixed<16,6>
Strategy: Stable
Interpreting Model
Topology:
Layer name: input_2, layer type: InputLayer, input shapes: [[None, 32, 32, 3]], output shape: [None, 32, 32, 3]
Layer name: fused_convbn_0, layer type: QConv2DBatchnorm, input shapes: [[None, 32, 32, 3]], output shape: [None, 30, 30, 16]
Layer name: conv_act_0, layer type: Activation, input shapes: [[None, 30, 30, 16]], output shape: [None, 30, 30, 16]
Layer name: pool_0, layer type: MaxPooling2D, input shapes: [[None, 30, 30, 16]], output shape: [None, 15, 15, 16]
Layer name: fused_convbn_1, layer type: QConv2DBatchnorm, input shapes: [[None, 15, 15, 16]], output shape: [None, 13, 13, 16]
Layer name: conv_act_1, layer type: Activation, input shapes: [[None, 13, 13, 16]], output shape: [None, 13, 13, 16]
Layer name: pool_1, layer type: MaxPooling2D, input shapes: [[None, 13, 13, 16]], output shape: [None, 6, 6, 16]
Layer name: fused_convbn_2, layer type: QConv2DBatchnorm, input shapes: [[None, 6, 6, 16]], output shape: [None, 4, 4, 24]
Layer name: conv_act_2, layer type: Activation, input shapes: [[None, 4, 4, 24]], output shape: [None, 4, 4, 24]
Layer name: pool_2, layer type: MaxPooling2D, input shapes: [[None, 4, 4, 24]], output shape: [None, 2, 2, 24]
Layer name: flatten_1, layer type: Reshape, input shapes: [[None, 2, 2, 24]], output shape: [None, 96]
Layer name: dense_0, layer type: QDense, input shapes: [[None, 96]], output shape: [None, 42]
Layer name: bn_dense_0, layer type: BatchNormalization, input shapes: [[None, 42]], output shape: [None, 42]
Layer name: dense_act_0, layer type: Activation, input shapes: [[None, 42]], output shape: [None, 42]
Layer name: dense_1, layer type: QDense, input shapes: [[None, 42]], output shape: [None, 64]
Layer name: bn_dense_1, layer type: BatchNormalization, input shapes: [[None, 64]], output shape: [None, 64]
Layer name: dense_act_1, layer type: Activation, input shapes: [[None, 64]], output shape: [None, 64]
Layer name: output_dense, layer type: Dense, input shapes: [[None, 64]], output shape: [None, 10]
Layer name: output_softmax, layer type: Softmax, input shapes: [[None, 10]], output shape: [None, 10]
Creating HLS model
WARNING: Cannot use "Latency" model strategy for fused_convbn_0 layer. Switching to "Resource" strategy.
Writing HLS project
WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.
WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.
Done
Let’s plot the model and profile the weights her too
hls4ml.model.profiling.numerical(model=qmodel, hls_model=hls_model_q)
hls4ml.utils.plot_model(hls_model_q, show_shapes=True, show_precision=True, to_file=None)
Interpreting Model
Topology:
Layer name: input_2, layer type: InputLayer, input shapes: [[None, 32, 32, 3]], output shape: [None, 32, 32, 3]
Layer name: fused_convbn_0, layer type: QConv2DBatchnorm, input shapes: [[None, 32, 32, 3]], output shape: [None, 30, 30, 16]
Layer name: conv_act_0, layer type: Activation, input shapes: [[None, 30, 30, 16]], output shape: [None, 30, 30, 16]
Layer name: pool_0, layer type: MaxPooling2D, input shapes: [[None, 30, 30, 16]], output shape: [None, 15, 15, 16]
Layer name: fused_convbn_1, layer type: QConv2DBatchnorm, input shapes: [[None, 15, 15, 16]], output shape: [None, 13, 13, 16]
Layer name: conv_act_1, layer type: Activation, input shapes: [[None, 13, 13, 16]], output shape: [None, 13, 13, 16]
Layer name: pool_1, layer type: MaxPooling2D, input shapes: [[None, 13, 13, 16]], output shape: [None, 6, 6, 16]
Layer name: fused_convbn_2, layer type: QConv2DBatchnorm, input shapes: [[None, 6, 6, 16]], output shape: [None, 4, 4, 24]
Layer name: conv_act_2, layer type: Activation, input shapes: [[None, 4, 4, 24]], output shape: [None, 4, 4, 24]
Layer name: pool_2, layer type: MaxPooling2D, input shapes: [[None, 4, 4, 24]], output shape: [None, 2, 2, 24]
Layer name: flatten_1, layer type: Reshape, input shapes: [[None, 2, 2, 24]], output shape: [None, 96]
Layer name: dense_0, layer type: QDense, input shapes: [[None, 96]], output shape: [None, 42]
Layer name: bn_dense_0, layer type: BatchNormalization, input shapes: [[None, 42]], output shape: [None, 42]
Layer name: dense_act_0, layer type: Activation, input shapes: [[None, 42]], output shape: [None, 42]
Layer name: dense_1, layer type: QDense, input shapes: [[None, 42]], output shape: [None, 64]
Layer name: bn_dense_1, layer type: BatchNormalization, input shapes: [[None, 64]], output shape: [None, 64]
Layer name: dense_act_1, layer type: Activation, input shapes: [[None, 64]], output shape: [None, 64]
Layer name: output_dense, layer type: Dense, input shapes: [[None, 64]], output shape: [None, 10]
Layer name: output_softmax, layer type: Softmax, input shapes: [[None, 10]], output shape: [None, 10]
Creating HLS model
WARNING: Cannot use "Latency" model strategy for fused_convbn_0 layer. Switching to "Resource" strategy.
Profiling weights (before optimization)
Weights for dense_0 are only zeros, ignoring.
Weights for dense_1 are only zeros, ignoring.
Profiling weights (final / after optimization)
Weights for dense_0 are only zeros, ignoring.
Weights for dense_1 are only zeros, ignoring.
For the 6-bit QKeras model, we see that different precisions are used for different layers.
Accuracy with bit-accurate emulation#
Let’s check that the hls4ml accuracy matches the original. This usually takes some time, so let’s do it over a reduced dataset
X_test_reduced = X_test[:3000]
Y_test_reduced = Y_test[:3000]
y_predict = model.predict(X_test_reduced)
y_predict_hls4ml = hls_model.predict(np.ascontiguousarray(X_test_reduced))
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y_predict_q = qmodel.predict(X_test_reduced)
y_predict_hls4ml_q = hls_model_q.predict(np.ascontiguousarray(X_test_reduced))
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import plotting
from sklearn.metrics import accuracy_score
def plotROC(Y, y_pred, y_pred_hls4ml, label="Model"):
accuracy_keras = float(accuracy_score(np.argmax(Y, axis=1), np.argmax(y_pred, axis=1)))
accuracy_hls4ml = float(accuracy_score(np.argmax(Y, axis=1), np.argmax(y_pred_hls4ml, axis=1)))
print("Accuracy Keras: {}".format(accuracy_keras))
print("Accuracy hls4ml: {}".format(accuracy_hls4ml))
fig, ax = plt.subplots(figsize=(9, 9))
_ = plotting.makeRoc(Y, y_pred, labels=['%i' % nr for nr in range(n_classes)])
plt.gca().set_prop_cycle(None) # reset the colors
_ = plotting.makeRoc(Y, y_pred_hls4ml, labels=['%i' % nr for nr in range(n_classes)], linestyle='--')
from matplotlib.lines import Line2D
lines = [Line2D([0], [0], ls='-'), Line2D([0], [0], ls='--')]
from matplotlib.legend import Legend
leg = Legend(ax, lines, labels=['Keras', 'hls4ml'], loc='lower right', frameon=False)
ax.add_artist(leg)
plt.figtext(0.2, 0.38, label, wrap=True, horizontalalignment='left', verticalalignment='center')
plt.ylim(0.01, 1.0)
plt.xlim(0.7, 1.0)
# Plot the pruned floating point model:
plotROC(Y_test_reduced, y_predict, y_predict_hls4ml, label="Keras")
# Plot the pruned and quantized QKeras model
plotROC(Y_test_reduced, y_predict_q, y_predict_hls4ml_q, label="QKeras")
Accuracy Keras: 0.8873333333333333
Accuracy hls4ml: 0.8876666666666667
Accuracy Keras: 0.8566666666666667
Accuracy hls4ml: 0.8573333333333333
Looks good! Let’s synthesize the models.
Logic synthesis#
This takes quite a while for CNN models, up to one hour for the models considered here. In the interest of time, we have therefore provided the neccessary reports for the models considered. You can also synthesize them yourself if you have time, and as usual follow the progress using tail -f pruned_cnn/vivado_hls.log and tail -f quantized_pruned_cnn/vivado_hls.log.
import os
os.environ['PATH'] = os.environ['XILINX_VIVADO'] + '/bin:' + os.environ['PATH']
synth = False # Only if you want to synthesize the models yourself (>1h per model) rather than look at the provided reports.
if synth:
hls_model.build(csim=False, synth=True, vsynth=True)
hls_model_q.build(csim=False, synth=True, vsynth=True)
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
Cell In[26], line 3
1 import os
----> 3 os.environ['PATH'] = os.environ['XILINX_VIVADO'] + '/bin:' + os.environ['PATH']
5 synth = False # Only if you want to synthesize the models yourself (>1h per model) rather than look at the provided reports.
6 if synth:
File /usr/share/miniconda/envs/hls4ml-tutorial/lib/python3.10/os.py:680, in _Environ.__getitem__(self, key)
677 value = self._data[self.encodekey(key)]
678 except KeyError:
679 # raise KeyError with the original key value
--> 680 raise KeyError(key) from None
681 return self.decodevalue(value)
KeyError: 'XILINX_VIVADO'
We extract the latency from the C synthesis, namely the report in <project_dir>/myproject_prj/solution1/syn/report/myproject_csynth.rpt. A more accurate latency estimate can be obtained from running cosim by passing hls_model.build(csim=False, synth=True, vsynth=True, cosim=True) ( = C/RTL cosimulation, synthesised HLS code is run on a simulator and tested on C test bench) but this takes a lot of time so we will skip it here.
The resource estimates are obtained from the Vivado logic synthesis, and can be extracted from the report in <project_dir>/vivado_synth.rpt. Let’s fetch the most relevant numbers:
def getReports(indir):
data_ = {}
report_vsynth = Path('{}/vivado_synth.rpt'.format(indir))
report_csynth = Path('{}/myproject_prj/solution1/syn/report/myproject_csynth.rpt'.format(indir))
if report_vsynth.is_file() and report_csynth.is_file():
print('Found valid vsynth and synth in {}! Fetching numbers'.format(indir))
# Get the resources from the logic synthesis report
with report_vsynth.open() as report:
lines = np.array(report.readlines())
data_['lut'] = int(lines[np.array(['CLB LUTs*' in line for line in lines])][0].split('|')[2])
data_['ff'] = int(lines[np.array(['CLB Registers' in line for line in lines])][0].split('|')[2])
data_['bram'] = float(lines[np.array(['Block RAM Tile' in line for line in lines])][0].split('|')[2])
data_['dsp'] = int(lines[np.array(['DSPs' in line for line in lines])][0].split('|')[2])
data_['lut_rel'] = float(lines[np.array(['CLB LUTs*' in line for line in lines])][0].split('|')[5])
data_['ff_rel'] = float(lines[np.array(['CLB Registers' in line for line in lines])][0].split('|')[5])
data_['bram_rel'] = float(lines[np.array(['Block RAM Tile' in line for line in lines])][0].split('|')[5])
data_['dsp_rel'] = float(lines[np.array(['DSPs' in line for line in lines])][0].split('|')[5])
with report_csynth.open() as report:
lines = np.array(report.readlines())
lat_line = lines[np.argwhere(np.array(['Latency (cycles)' in line for line in lines])).flatten()[0] + 3]
data_['latency_clks'] = int(lat_line.split('|')[2])
data_['latency_mus'] = float(lat_line.split('|')[2]) * 5.0 / 1000.0
data_['latency_ii'] = int(lat_line.split('|')[6])
return data_
from pathlib import Path
import pprint
data_pruned_ref = getReports('pruned_cnn')
data_quantized_pruned = getReports('quantized_pruned_cnn')
print("\n Resource usage and latency: Pruned")
pprint.pprint(data_pruned_ref)
print("\n Resource usage and latency: Pruned + quantized")
pprint.pprint(data_quantized_pruned)
We see that the latency is of around 5 microseconds for both the quantized and the unquantized model, but that the resources are signifcantly reduced using QKeras.
Congratulations! You have now reached the end of this notebook. If you have some spare time, you can have a look at the bonus exercise below, where you will learn how to perform a bayesian optimization over the QKeras quantizers in order to obtain an optimally heterogeneously quantized model.
Bonus exercise: Automatic quantization with AutoQKeras#
In this bonus exercise, you will learn how to find the optimal heterogeneously quantized model using AutoQKeras. For more details, you can look at the AutoQKeras notebook.
Let’s first check the estimated energy consumption of the QKeras 6-bit model using QTools. By setting for_reference=True you can print out the unquantized model energy consumption and compare the two. Note that this only works for QKeras layers.
filters_per_conv_layer = [16, 16, 24]
neurons_per_dense_layer = [42, 64]
x = x_in = Input(input_shape)
for i, f in enumerate(filters_per_conv_layer):
print(('Adding convolutional block {} with N={} filters').format(i, f))
x = Conv2D(
int(f),
kernel_size=(3, 3),
strides=(1, 1),
kernel_initializer='lecun_uniform',
kernel_regularizer=l1(0.0001),
use_bias=False,
name='conv_{}'.format(i),
)(x)
x = BatchNormalization(name='bn_conv_{}'.format(i))(x)
x = Activation('relu', name='conv_act_%i' % i)(x)
x = MaxPooling2D(pool_size=(2, 2), name='pool_{}'.format(i))(x)
x = Flatten()(x)
for i, n in enumerate(neurons_per_dense_layer):
print(('Adding dense block {} with N={} neurons').format(i, n))
x = Dense(n, kernel_initializer='lecun_uniform', kernel_regularizer=l1(0.0001), name='dense_%i' % i, use_bias=False)(x)
x = BatchNormalization(name='bn_dense_{}'.format(i))(x)
x = Activation('relu', name='dense_act_%i' % i)(x)
x = Dense(int(n_classes), name='output_dense')(x)
x_out = Activation('softmax', name='output_softmax')(x)
baseline_model = Model(inputs=[x_in], outputs=[x_out], name='keras_baseline')
LOSS = tf.keras.losses.CategoricalCrossentropy()
OPTIMIZER = tf.keras.optimizers.Adam(learning_rate=3e-3, beta_1=0.9, beta_2=0.999, epsilon=1e-07, amsgrad=True)
baseline_model.compile(loss=LOSS, optimizer=OPTIMIZER, metrics=["accuracy"])
from qkeras import print_qstats
# for automatic quantization
import pprint
from qkeras.autoqkeras import *
from qkeras import *
from qkeras.utils import model_quantize
from qkeras.qtools import run_qtools
from qkeras.qtools import settings as qtools_settings
from tensorflow_model_optimization.python.core.sparsity.keras import pruning_wrapper
from qkeras import quantized_bits
from qkeras import QDense, QActivation
q = run_qtools.QTools(
baseline_model,
process="horowitz",
source_quantizers=[quantized_bits(16, 5, 1)],
is_inference=True,
weights_path=None,
keras_quantizer="fp16",
keras_accumulator="fp16",
for_reference=False,
)
q.qtools_stats_print()
energy_dict = q.pe(
weights_on_memory="fixed", activations_on_memory="fixed", min_sram_size=8 * 16 * 1024 * 1024, rd_wr_on_io=False
)
# get stats of energy distribution in each layer
energy_profile = q.extract_energy_profile(qtools_settings.cfg.include_energy, energy_dict)
# extract sum of energy of each layer according to the rule specified in
# qtools_settings.cfg.include_energy
total_energy = q.extract_energy_sum(qtools_settings.cfg.include_energy, energy_dict)
pprint.pprint(energy_profile)
print()
print("Total energy: {:.6f} uJ".format(total_energy / 1000000.0))
Now, lets use AutoQKeras to find an optimally heterogeneously quantized model for us. For more details, check the AutoQKeras tutorial linked above. As baseline model, we’ll use the pruned floating point Keras model from above.
# These are the quantizers we'll test in the bayesian optimization
quantization_config = {
"kernel": {
"quantized_bits(2,0,1,alpha=1.0)": 2,
"quantized_bits(4,0,1,alpha=1.0)": 4,
"quantized_bits(6,0,1,alpha=1.0)": 6,
"quantized_bits(8,0,1,alpha=1.0)": 8,
},
"bias": {
"quantized_bits(2,0,1,alpha=1.0)": 2,
"quantized_bits(4,0,1,alpha=1.0)": 4,
"quantized_bits(6,0,1,alpha=1.0)": 6,
"quantized_bits(8,0,1,alpha=1.0)": 8,
},
"activation": {
"quantized_relu(3,1)": 3,
"quantized_relu(4,2)": 4,
"quantized_relu(8,2)": 8,
"quantized_relu(8,4)": 8,
"quantized_relu(16,6)": 16,
},
"linear": {
"quantized_bits(2,0,1,alpha=1.0)": 2,
"quantized_bits(4,0,1,alpha=1.0)": 4,
"quantized_bits(6,0,1,alpha=1.0)": 6,
"quantized_bits(8,0,1,alpha=1.0)": 8,
},
}
# These are the layer types we will quantize
limit = {
"Dense": [8, 8, 16],
"Conv2D": [8, 8, 16],
"Activation": [16],
}
# Use this if you want to minimize the model bit size
goal_bits = {
"type": "bits",
"params": {
"delta_p": 8.0, # We tolerate up to a +8% accuracy change
"delta_n": 8.0, # We tolerate down to a -8% accuracy change
"rate": 2.0, # We want a x2 times smaller model
"stress": 1.0, # Force the reference model size to be smaller by setting stress<1
"input_bits": 8,
"output_bits": 8,
"ref_bits": 8,
"config": {"default": ["parameters", "activations"]},
},
}
# Use this if you want to minimize the model energy consumption
goal_energy = {
"type": "energy",
"params": {
"delta_p": 8.0,
"delta_n": 8.0,
"rate": 2.0,
"stress": 1.0,
"process": "horowitz",
"parameters_on_memory": ["sram", "sram"],
"activations_on_memory": ["sram", "sram"],
"rd_wr_on_io": [False, False],
"min_sram_size": [0, 0],
"source_quantizers": ["fp32"],
"reference_internal": "int8",
"reference_accumulator": "int32",
},
}
run_config = {
"goal": goal_energy,
"quantization_config": quantization_config,
"learning_rate_optimizer": False,
"transfer_weights": False, # Randomely initialize weights
"mode": "bayesian", # This can be bayesian,random,hyperband
"seed": 42,
"limit": limit,
"tune_filters": "layer",
"tune_filters_exceptions": "^output",
"distribution_strategy": None,
"max_trials": 5, # Let's just do 5 trials for this demonstrator, ideally you should do as many as possible
}
from qkeras.autoqkeras import AutoQKeras
autoqk = AutoQKeras(baseline_model, output_dir="autoq_cnn", metrics=["acc"], custom_objects={}, **run_config)
autoqk.fit(train_data, validation_data=val_data, epochs=15)
aqmodel = autoqk.get_best_model()
print_qmodel_summary(aqmodel)
# Train for the full epochs
callbacks = [
tf.keras.callbacks.EarlyStopping(patience=10, verbose=1),
tf.keras.callbacks.ReduceLROnPlateau(monitor='val_loss', factor=0.5, patience=3, verbose=1),
]
start = time.time()
history = aqmodel.fit(train_data, epochs=n_epochs, validation_data=val_data, callbacks=callbacks, verbose=1)
end = time.time()
print('\n It took {} minutes to train!\n'.format((end - start) / 60.0))
# This model has some remnants from the optimization procedure attached to it, so let's define a new one
aqmodel.save_weights("autoqkeras_cnn_weights.h5")
layers = [l for l in aqmodel.layers]
x = layers[0].output
for i in range(1, len(layers)):
x = layers[i](x)
new_model = Model(inputs=[layers[0].input], outputs=[x])
LOSS = tf.keras.losses.CategoricalCrossentropy()
OPTIMIZER = tf.keras.optimizers.Adam(learning_rate=3e-3, beta_1=0.9, beta_2=0.999, epsilon=1e-07, amsgrad=True)
new_model.compile(loss=LOSS, optimizer=OPTIMIZER, metrics=["accuracy"])
new_model.summary()
new_model.load_weights("autoqkeras_cnn_weights.h5")
print_qmodel_summary(new_model)
Let’s check what the best heterogeneously quantized model looks like (keep in mind we only did a few trials, the optimization obviosuly didn’t have time to converge at the minimum but yo get the idea!)
hls_config_aq = hls4ml.utils.config_from_keras_model(new_model, granularity='name')
hls_config_aq['Model']['ReuseFactor'] = 1
hls_config_aq['Model']['Precision'] = 'ap_fixed<16,6>'
hls_config_aq['LayerName']['output_softmax']['Strategy'] = 'Stable'
plotting.print_dict(hls_config_aq)
cfg_aq = hls4ml.converters.create_config(backend='Vivado')
cfg_aq['IOType'] = 'io_stream' # Must set this if using CNNs!
cfg_aq['HLSConfig'] = hls_config_aq
cfg_aq['KerasModel'] = new_model
cfg_aq['OutputDir'] = 'autoqkeras_cnn/'
cfg_aq['XilinxPart'] = 'xcu250-figd2104-2L-e'
hls_model_aq = hls4ml.converters.keras_to_hls(cfg_aq)
hls_model_aq.compile()
y_predict_aq = aqmodel.predict(X_test_reduced)
y_predict_hls4ml_aq = hls_model_aq.predict(np.ascontiguousarray(X_test_reduced))
accuracy_keras = float(accuracy_score(np.argmax(Y_test_reduced, axis=1), np.argmax(y_predict_aq, axis=1)))
accuracy_hls4ml = float(accuracy_score(np.argmax(Y_test_reduced, axis=1), np.argmax(y_predict_hls4ml_aq, axis=1)))
print("Accuracy AutoQ Keras: {}".format(accuracy_keras))
print("Accuracy AutoQ hls4ml: {}".format(accuracy_hls4ml))
The accuracy is slightly lower for this heterogeneously quantized model. Due to some randomness in the optimization procedure, you’re going to have to synthesize this one yourself!
synth = True
if synth:
hls_model_aq.build(csim=False, synth=True, vsynth=True)
data_autoq = getReports('autoq_cnn')
print("\n Resource usage and latency: AutoQ")
pprint.pprint(data_autoq)